Thesis final - after defense-7 - Jacobs University
Thesis final - after defense-7 - Jacobs University
Thesis final - after defense-7 - Jacobs University
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A proteome wide approach to understand<br />
chromatography behavior on hydrophobic<br />
adsorbents<br />
by<br />
Nawab Ali<br />
A <strong>Thesis</strong> submitted in partial fulfillment<br />
of the requirements for the degree of<br />
Doctor of Philosophy<br />
in Biochemical Engineering<br />
Approved Dissertation Committee<br />
__________________________________<br />
Prof. Dr. Marcelo Fernández-Lahore<br />
Prof. Dr. Matthias Ullrich<br />
Dr. Abid Riaz Ahmad<br />
Date of Defense:<br />
June 05 th , 2012<br />
School of Engineering and Science
Acknowledgement<br />
All praise for Almighty ALLAH, Without Allah’s help, I would not be able to achieve<br />
anything in my life.<br />
I am greatly honored to owe my deepest thanks to my thesis supervisor Prof. Dr. Marcelo<br />
Fernández-Lahore for giving me the opportunity to carry out my PhD work under his kind<br />
supervision and accepting me in his international group. I greatly appreciate his valuable<br />
suggestions and supervision in my PhD work. I am highly thankful to him for accepting my<br />
wife as a graduate student under his kind supervision. He has given me an opportunity to<br />
perform my tasks independently which increased my curiosity for research and which<br />
accomplishes the true spirits of PhD. I am thankful for his student friendly and cooperative<br />
attitude in the last four years.<br />
I am also thankful to Prof. Dr. Matthias Ullrich and Dr. Abid Riaz Ahmad for their comments<br />
and nice suggestions for the improvement of the thesis.<br />
I also want to thank and acknowledge Muhammed Mobashir and Abdul Wakeel for helping<br />
me in the bioinformatics part of this work.<br />
I convey my heartiest acknowledgements to my group fellows Aasim, Zia, Nadia, Noor Shad,<br />
Farhat, Amir, Tuhidul Islam, Sirma, Amira, Rajesh, Dr Rami, Dr. Sonja, Miss. Nina, Antonio,<br />
Doreen, Iza, Naveen, Prasad, Rodrigo and Zumrad.<br />
Words are lacking to present my feeling for the prayers and struggles done by my parents<br />
throughout my life from childhood until now. I am now in this position due to my father<br />
continuous guidance and efforts during my education. I am also thankful to my brother<br />
Naveed Ali, sisters and brother in law; Sadeeq and Sabir for their love and care at each step of<br />
my life. I also want to acknowledge my other relatives and uncles Mumtaz Ali, Ihsan Ali,<br />
Qamer Zaman and my cousins Nazrali, Mohsin, Jenab, Aasim, Zafar, Amin, Khaild.<br />
I
I also want to mention the efforts of my wife Nadia for her patience and support during my<br />
PhD studies when I was in tough time of having funding and other research problems. I am<br />
also thankful to her for letting me free from other homely responsibilities when I was busy in<br />
my research work.<br />
I also want to convey my heartiest and sincerest acknowledgements to my closest friends<br />
Ammar, Qasim, Aasim, Yaqoob, Noor Muhammad saib, Abunaser saib, Farhan, Hamish saib,<br />
Farrukh saib, Roohalamin saib, Noor Shad, Zia, Wakeel, Sadiq, Amna, Farhat, Qazi, Hazir,<br />
Tariq, Inam, Tahir, Ali, Imran, Rehan, Saleem, Shahzad.<br />
Finally, I wish to thank Kohat <strong>University</strong> of Science and Technology (KUST), Khyber<br />
Pukhtunkhwa, Pakistan and Higher Education Commission, Islamabad, Pakistan for providing<br />
me opportunity for higher studies abroad.<br />
II
Nomenclature<br />
S. cerevisiae Saccharomyces cerevisiae<br />
HIC<br />
Hydrophobic interaction chromatography<br />
ASH<br />
Average surface hydrophobicity<br />
PDB<br />
Protein databank<br />
AH<br />
Average hydrophobicity<br />
ASA<br />
Accessible surface area<br />
CW<br />
Cowan-Whittaker scale<br />
MJ<br />
Miyazawa-Jernigan scale<br />
T<br />
Tanford scale<br />
AF<br />
Average flexibility<br />
CV<br />
Column volume<br />
mAU<br />
Milli absorbance unit<br />
IEF<br />
Isoelectric focussing<br />
TEMED<br />
N, N, N, N-tetramethyl-ethylenediamine<br />
DTT<br />
Dithiothreitol<br />
Mili Q water water purified with millipore system<br />
SDS-PAGE Sodium dodecyl polyacrylamide gel electrophoresis<br />
MALDI-ToF-MS Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass spectrometry<br />
2-D PAGE Two dimensional polyacrylamide gel electrophoresis<br />
PS<br />
Phenyl-Sepharose<br />
SP<br />
Source-Phenyl<br />
TP<br />
Toyopearl-Phenyl<br />
TB<br />
Toyopearl-Butyl<br />
TH<br />
Toyopearl-Hexyl<br />
TE<br />
Toyopearl-Ether<br />
S j<br />
S i<br />
Sum of total surface area of the proteins<br />
Sum of accessible surface area of for all the amino acids of type i<br />
r i Superficial area of amino acid for all the amino acids of class i.<br />
φ<br />
i<br />
Hydrophobicity of amino acids for all the amino acids of class i<br />
Φ surface Average surface hydrophobicity<br />
Mr<br />
pI<br />
Molecular weight of the protein<br />
Isoelectric point of the protein<br />
III
Dedication<br />
To my father Haji Sher Afzal and mother Kamkhwaba for their continuous support and<br />
efforts during my education and in every aspect of my life.<br />
IV
Abstract<br />
The separation behavior of the Saccharomyces cerevisiae cell proteome was explored by<br />
hydrophobic interaction chromatography (HIC). Several beaded adsorbents were studied. A<br />
first group of HIC adsorbents (n = 3) harbored the same ligand moiety (i.e., Phenyl) but<br />
presented differences in the chemical nature of the matrix backbone. On the other hand, a<br />
second group of adsorbents (n = 3) presented the same chemical structure (i.e. based on<br />
polymethacrylate; Toyopearl / TP) but differed in the chemical nature of the HIC ligands.<br />
Chromatography runs were performed under typical conditions employing ammonium sulfate<br />
/ phosphate buffer (pH = 7.5) as a mobile phase. Chromatography fractions were collected<br />
and further analyzed by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE);<br />
selected spots were identified by MALDI-ToF-MS and database search.<br />
A comparative analysis of protein separation behavior is presented as a function of the<br />
adsorbent type. As expected, TP-Hexyl and TP-Butyl showed increased protein retention in<br />
comparison with TP-Ether. Moreover, an influence of protein size and isoelectric point (pI)<br />
was also revealed. Larger and / or neutral proteins showed increased while acidic (pI < 6) and<br />
basic (pI > 8) proteins depicted decreased or increased retention, depending on the adsorbenttype.<br />
Protein characteristics such as average surface hydrophobicity, average hydrophobicity<br />
average polarity, average bulkiness and average flexibility were tabulated for the identified<br />
spots and correlated with chromatography behavior. The proteins properties revealed limited<br />
ability to differentiate among the ligands and base supports for their hydrophobicity. This<br />
work -for the first time- studied the separation behavior of a real and complex cell proteome<br />
as a function of commercially available HIC adsorbents. The mentioned results will favor the<br />
understanding and the application of HIC, a method of choice in many industrial bioseparation<br />
schemes.<br />
I
Table of Contents<br />
1. Introduction ............................................................................................................................ 1<br />
1.1. Introduction to Biotechnology ........................................................................................ 1<br />
1.2. Downstream processing .................................................................................................. 2<br />
1.3. Hydrophobic interaction chromatography (HIC) ............................................................ 4<br />
1.4. Factors affecting protein chromatography behavior in HIC ........................................... 7<br />
1.4.1. Effect of mobile phase parameters ........................................................................... 7<br />
1.4.2. Effect of stationary phases in HIC ......................................................................... 10<br />
1.4.3. Effect of protein properties in HIC ........................................................................ 12<br />
1.4.4. The comparison between HIC and RPC ................................................................ 13<br />
1.5. Analysis of the yeast proteome as biotech feedstock .................................................... 15<br />
1.6. Mass spectrometry and database searching ................................................................... 16<br />
1.7. The in silico Approach .................................................................................................. 18<br />
1.8. Goal of the work ............................................................................................................ 21<br />
2. Experimental approach ......................................................................................................... 24<br />
2.1. Materials ........................................................................................................................ 24<br />
2.2. Experimental strategy .................................................................................................... 24<br />
2.2.1. Cell Cultivation ...................................................................................................... 26<br />
2.2.2. Preparation of the crude extract ............................................................................. 26<br />
2.2.3. Chromatographic fractionation .............................................................................. 26<br />
2.2.4. Membrane dialysis ................................................................................................. 27<br />
2.2.5. Two dimensional polyacrylamide gel electrophoresis (2-D PAGE) ...................... 27<br />
2.2.6. Protein content of the fractions .............................................................................. 29<br />
2.2.7. Protein identification by MALDI-ToF-MS ............................................................ 29<br />
2.2.8. Characterization of proteins utilizing Bioinformics ............................................... 31<br />
3. Results and Discussion ......................................................................................................... 35<br />
3.1. Influence of the different ligand chemistries during hydrophobic interaction<br />
chromatography ........................................................................................................................ 35<br />
3.1.1. Summary .................................................................................................................... 35<br />
3.1.2. Scientific background of the approach ....................................................................... 36<br />
3.1.2.1. Hydrophobic adsorbents ...................................................................................... 36<br />
3.1.2.2. Protein properties in the context of HIC ............................................................. 39<br />
3.1.2.3. Towards an in silico approach for the downstream processing of bioproducts .. 43<br />
2
3.1.3. Results and Discussion ............................................................................................... 45<br />
3.1.3.1. Chromatographic profiles of different ligands .................................................... 45<br />
3.1.3.2. Two dimensional gel electrophoresis and proteins identification ....................... 47<br />
3.1.3.3. Influence of the ligands chemistries and average protein properties .................. 55<br />
3.1.3.3.1. Average hydrophobicity (AH) ............................................................................. 55<br />
3.1.3.3.2. Average polarity (AP) .......................................................................................... 59<br />
3.1.3.3.3. Average flexibility (AF)....................................................................................... 62<br />
3.1.3.3.4. Average Bulkiness (AB) ...................................................................................... 65<br />
3.1.3.3.5. Average surface hydrophobicity (ASH)............................................................... 66<br />
3.1.3.4. Comparative studies of the hydrophobic ligands based on average protein<br />
properties .......................................................................................................................... 68<br />
3.1.3.5. Application note .................................................................................................. 70<br />
3.1.4. Partial conclusions ...................................................................................................... 71<br />
3.2. Influence of the different base support chemistries during hydrophobic interaction<br />
chromatography ........................................................................................................................ 72<br />
3.2.1. Summary .................................................................................................................... 72<br />
3.2.2. Chemistry of the base supports .................................................................................. 73<br />
3.2.3. Results and Discussion ............................................................................................... 75<br />
3.2.3.1. Chromatographic fractionation ........................................................................... 75<br />
3.2.3.2. The electrophoretic separation and proteins identification ................................. 78<br />
3.2.3.3. Influence of the base support chemistries and average protein properties .......... 85<br />
3.2.3.3.1. Average hydrophobicity (AH) ............................................................................. 85<br />
3.2.3.3.2. Average polarity (AP) .......................................................................................... 89<br />
3.2.3.3.3. Average surface hydrophobicity (ASH)............................................................... 93<br />
3.2.3.3.4. Other protein properties affecting the chromatographic behavior ....................... 95<br />
3.2.3.4. Comparative studies of the different base supports based on average protein<br />
properties .......................................................................................................................... 98<br />
3.2.3.5. Application note ................................................................................................ 100<br />
3.2.3. Partial conclusions .................................................................................................... 101<br />
3.3. Protein distributions on 2-D gels: the influence of molecular weight and isoelectric point<br />
coordinates ............................................................................................................................. 102<br />
3.3.1. Summary .................................................................................................................. 102<br />
3.3.2. Results and Discussion ............................................................................................. 103<br />
3.3.2.1. Unfolding of proteins during HIC ..................................................................... 103<br />
3
3.3.2.2. Protein distributions on 2-D gels ....................................................................... 104<br />
3.3.2.3. The effect of protein molecular mass on the chromatographic behavior .......... 106<br />
3.3.2.3. The influence of protein isoelectric point on the chromatographic behavior ... 108<br />
3.3.3. Partial conclusions .................................................................................................... 111<br />
4. Conclusions and Remarks .................................................................................................. 113<br />
4.1. General conclusions and remarks ................................................................................ 113<br />
4.2. Influence of different ligands ...................................................................................... 113<br />
4.3. Influence of different base supports ............................................................................ 113<br />
4.4. Effect of protein properties on separation behavior during chromatography ............. 114<br />
4.5. 2-D gels analysis ......................................................................................................... 115<br />
4.5. Additional conclusions ................................................................................................ 115<br />
4.6. Recommendations for future work .............................................................................. 116<br />
5. References .......................................................................................................................... 117<br />
4
Chapter 1<br />
INTRODUCTION<br />
5
Chapter 1<br />
1. Introduction<br />
1.1. Introduction to Biotechnology<br />
The term biotechnology is a fusion of biology and technology which is concerned with the<br />
utilization of biological agents to produce useful products of human use (1). Biotechnology<br />
has been known for thousands of years, where humans have used it for the food purposes e.g.<br />
for the selective breeding of crops and livestock, production of wine, beer, cheese and other<br />
dairy products. In the early twentieth century, a new era of the modern biotechnology started<br />
with the production of complex organic molecules like antibiotics, vaccines and monoclonal<br />
antibodies. The production of these macromolecules has been optimized by the improved<br />
fermentation procedures and novel downstream processing approaches (2). The discoveries<br />
for the new products have made the pharmaceutical industry as one of the fastest growing<br />
sectors in the global economy. The pharmaceutical, agrichemical and biotechnology related<br />
products have the billions dollar annual sale in market excluding the food and beverages (3, 4).<br />
Proteins and enzymes are the main biopharmaceutical products and have several applications<br />
in food and medical biotechnology (5-7). The production of human biopharmaceuticals was in<br />
limited supply and very costly before the recombinant DNA technology revolution. The<br />
proteins were commonly used before from the plant's and the animal's origin directly which<br />
was mostly not feasible due to the resulting immune responses against foreign antigens.<br />
However, genetic engineering made it possible to clone the responsible genes in microbial<br />
cells and produce the larger amount of protein of interest. Bioprocess can be usually divided<br />
into two main parts such as upstream and downstream processing. Upstream processing is the<br />
successful fermentation step and downstream processing is the purification plan of the desired<br />
protein at a large scale. Although a lot of work has been done for the fermentation<br />
optimization and development of recombinant strain producing the biomolecules, but the<br />
purification process is still a major bottleneck in order to fulfil the market demands. The main<br />
1
Chapter 1<br />
objective is to obtain the highest yield and purity of the target protein in the minimum<br />
possible resources by downstream processing (8).<br />
1.2. Downstream processing<br />
Purification is the next step <strong>after</strong> the production of a biomolecule through successful<br />
fermentation. The isolation and purification of a biomolecule at industrial level to a form,<br />
suitable for its intended use is called downstream processing. The products of biotechnology<br />
include whole cells, organic acids, amino acids, antibiotics, industrial enzymes, gums,<br />
vaccines etc. The different separation methods can be applied for the recovery and<br />
purification of the biomolecules depending on their sizes and natures (2). The purification of<br />
the desired product needs certain downstream processing steps, which accomplishes 60% of<br />
the total cost, excluding the cost to purchase raw materials (9, 10). Several expert systems<br />
have been developed for the protein purification processes based on the use of empirical<br />
knowledge that is not exercised in nature and is typically used by experts (11-14). The<br />
number of steps varies ranging from eight to fourteen in an expert system of common practice.<br />
Additional steps will cause an increase in the cost and also a decrease in the yield. As the<br />
number of steps for any downstream process is increasing, the product loss will occur. For an<br />
efficient downstream processing, it is important to design a bioprocess with the minimum<br />
possible steps. This will increase the yield and purity of the product to an acceptable range<br />
(15-18). The bioprocess has usually two main steps; recovery and purification. Recovery is<br />
composed of a collection of cell proteome from disrupted cells, while purification is the<br />
chromatographic fractionation of the cell proteome for the isolation of desired protein. The<br />
target protein has to be isolated from other impurities such as DNA, RNA and cellular debris,<br />
if the product exists intracellular. Minute amount of other impurities can be separated by<br />
different chromatographies to avoid any toxic effects in the <strong>final</strong> product coming from a<br />
biological mixture (8). It is also important to study other contaminants available in the host<br />
2
Chapter 1<br />
cell proteome to facilitate the separation of the target protein in the real bioprocess. Several<br />
expert systems have been proposed to link the efficiency of a unit operation with the protein<br />
properties and to separate the target protein from the other discrete number of contaminants.<br />
Most of these approaches to understand the chromatographic behavior of proteins were<br />
limited to the use of model proteins instead of protein mixtures in the host cell proteome. No<br />
comprehensive struggle has been made before to define the contaminant proteome of real<br />
recombinant host in order to investigate the individual contaminant behavior during its<br />
chromatographic separation. A paradigm shift from focussing on protein product to defining<br />
the major contaminant might give a more realistic picture and could also make this approach<br />
more suitable. The characterization of host related proteins is also important to maintain the<br />
product quality (9, 19).<br />
Chromatography is an important unit operation of any downstream process which helps to<br />
remove the contaminants during the purification of biological materials. Chromatographic<br />
separation of protein mixtures is becoming one of the most effective and widely used means<br />
of purifying individual proteins. The chromatographic methods for protein separations are<br />
based on the general physicochemical properties of the proteins (Figure 1) (20). Minor<br />
differences between various proteins such as size, charge and hydrophobicity can be used to<br />
purify one protein from the other proteins during chromatography. Chromatographic<br />
separations are usually based on protein characteristics such as charge (ion exchange<br />
chromatography), size (size exclusion chromatography), isoelectric point (chromato focussing)<br />
affinity (affinity chromatography) and hydrophobicity (hydrophobic interaction<br />
chromatography and reverse phase chromatography). Hydrophobic interaction<br />
chromatography (HIC) is a chromatographic method based on protein hydrophobicity (4).<br />
HIC was considered as more significant than other chromatographic methods, due to its mild<br />
hydrophobic nature and feasibility for the separation of less stable proteins (21). HIC is a<br />
3
Chapter 1<br />
chromatographic method of choice in downstream processing and have been widely used for<br />
the purification of proteins, enzymes, antibiotics and vaccines (14, 22, 29).<br />
Figure 1: The chromatographic methods based on different protein properties (Adapted from<br />
Ref. 20).<br />
1.3. Hydrophobic interaction chromatography (HIC)<br />
In industrial biotechnology, the purification of the desired product is very important <strong>after</strong><br />
successful fermentation. There are several chromatography methods available for protein<br />
purification based on the physicochemical properties of the proteins. However, HIC can be<br />
preferred due to its ability of rapid separation, high resolution and less chances of protein<br />
denaturation (23, 24). HIC is based on the reversible interactions between the hydrophobic<br />
surface patch of a protein and the hydrophobic surface of a chromatographic medium at<br />
moderately high salt concentrations. Proteins contain a variety of amino acids having<br />
hydrophilic and hydrophobic residues. Many of the hydrophobic residues are buried in the<br />
interior of the proteins, but on exposure to a solvent, a significant proportion of them can<br />
appear on the surface. However, the ratio of surface hydrophobicity differs in different<br />
4
Chapter 1<br />
proteins. It is this difference which can be exploited in HIC for protein separations. Protein<br />
hydrophobicity is the main physicochemical property which has a decisive role in retention<br />
during chromatography. There is no universally agreed single method to calculate protein<br />
hydrophobicity, however there is a general agreement that it can be determined by the<br />
hydrophobic contribution of the amino acid residues (25, 26). The operating conditions such<br />
as mobile phase properties (salt type, ionic strength and pH), stationary phase characteristics<br />
(chemical nature of the backbone, type of hydrophobic ligand and substitution level of the<br />
resin) and temperature play important roles in HIC (27, 28). Hydrophobic interactions are the<br />
most important non covalent forces which have the ability to maintain the native structures of<br />
the proteins at high salts concentrations. The basic principle of the HIC is different from size<br />
exclusion and ion exchange chromatography (IEC) and thus can be used for the products<br />
which have no possibility to be separated by these techniques. HIC is preferred over other<br />
chromatographic methods if proteins of similar size or isoelectric point coordinates have to be<br />
separated from each other. HIC can also be used as an ideal step, <strong>after</strong> the separation of<br />
biomaterials during IEC at high salt concentrations. Due to the entries of several new HIC<br />
media and better understanding of the factors affecting the retention, HIC has become one of<br />
the most widely used method for purification of proteins such as serum proteins, membrane<br />
proteins, nuclear proteins, receptors and recombinant proteins for research and industrial<br />
applications (14, 23, 29). The enzymes of commercial importance such as lipases, amylases,<br />
streptokinases can be purified utilizing HIC (30). Several expert systems have been produced<br />
to exploit the physicochemical properties of the proteins in order to separate the desired<br />
product from other contaminants in HIC (31, 32). In most of these approaches, the prediction<br />
of protein retention was limited to the use of model proteins instead of the host cell proteome<br />
(33). Studies of the host cell proteome which produces the product of interest can help in the<br />
design of the process; give in depth knowledge of the target and undesired proteins for the<br />
efficient downstream processing. No large scale effort has been made to study the<br />
5
Chapter 1<br />
contaminants which have close similarity to the target protein in the host cell proteome (19).<br />
In order to do so, one has to design the experiments to analyze the adsorption affinity of the<br />
adsorbents with a natural host.<br />
The first report about HIC was done by Sheppard and Tiselius observing the retention of dye<br />
in the presence of sulfate and phosphate solutions and was called salting out chromatography<br />
(34). Afterwards, Shalteil and Er-el used the term hydrophobic chromatography, Hofstee<br />
named the method as hydrophobic adsorption chromatography (34, 35). Finally, Hjerten in<br />
1973 described the method as HIC (36). Porath et al. discovered that hydrophobic adsorption<br />
was enhanced by using salts like sodium chloride or phosphate chloride and named the<br />
method as salt promoted adsorption chromatography (34).<br />
A typical chromatogram obtained during my experiments has been presented in Figure 2. To<br />
facilitate the hydrophobic interactions and adsorption of proteins, it is important to load the<br />
proteins on a column with a buffer of high salt concentrations, followed by elution with a<br />
linear or a stepwise decrease in the salt concentration (37). Linear gradient can be applied<br />
during chromatographic separation of biological mixtures to separate the proteins of diverse<br />
nature. After sample application, the proteins with no binding affinity will elute in flow<br />
through and washing step. The proteins bound with the adsorbent will elute according to their<br />
binding capabilities and the extent of protein hydrophobicity. The proteins with less surface<br />
hydrophobic residues will elute at the start of the chromatography. In contrast, the proteins<br />
with high hydrophobic residues on the surface will be tightly bound and will elute at the end<br />
of the chromatography. The highly hydrophobic proteins are usually tightly bound to the<br />
column and can be removed by salt free buffer or 20% ethanol. As the surface hydrophobicity<br />
of the proteins is increasing, the retention time of the proteins will increase accordingly<br />
during chromatography.<br />
6
Chapter 1<br />
Salt conc. decreasing<br />
(Gradient elution)<br />
Sample application.<br />
Abs.<br />
280 nm<br />
Unbound proteins<br />
elute before gradient.<br />
Tightly bound<br />
proteins elute in salt<br />
free conditions<br />
Column Volume (CV)<br />
Figure 2: A typical HIC chromatogram describing the chromatographic fractionation during HIC.<br />
1.4. Factors affecting protein chromatography behavior in HIC<br />
1.4.1. Effect of mobile phase parameters<br />
The mobile phase parameters affecting the protein retention are usually ionic strength, type of<br />
salts and buffer pH. Protein adsorption on hydrophobic adsorbents is usually favored by<br />
moderately high salt concentrations. The concentration of the salt should be according to the<br />
binding strength of the proteins and adsorbents and below the concentration causing<br />
precipitation of proteins. The optimum concentrations of salts are usually between 0.75 M and<br />
2.0 M with ammonium sulfate and 1.0 M to 4.0 M with sodium chloride (27). The different<br />
types of salts can be divided into kosmotrophic and chaotrophic salts depending on their<br />
ability of hydrophobic interactions. The chaotrophic salts (magnesium sulfate and magnesium<br />
chloride) have less capability to bind water molecules which increases the inclusion of water<br />
molecules on the protein and ligand surface and thus decreases hydrophobic interactions<br />
7
Chapter 1<br />
(salting in effect). While kosmotrophic salts (sodium, potassium or ammonium sulfate) bound<br />
tightly to water molecules which facilitate the exclusion of water molecules on the proteins<br />
and ligand surface and thus promotes hydrophobic interactions (salting out effect) (Figure 3)<br />
(20, 38).<br />
Figure 3: The mechanism of hydrophobic interaction chromatography (Adapted from Ref. 20)<br />
It has been reported by Hjerten that the main force which arises during the dispersion of water<br />
molecules is an increase in the entropy of the system. It has been stated that the hydrophobic<br />
ligand-protein interaction was thermodynamically favorable process (36, 39). The Hofmeister<br />
(lyotrophic) series of cations and anions was proposed before, starting with those favoring the<br />
interactions to those that disfavor the hydrophobic interactions. For anions and cations, the<br />
series is given below.<br />
Anions; PO 4<br />
3-<br />
> SO 4<br />
2-<br />
> CH 3 COO - > Cl - > Br - > NO 3<br />
-<br />
> ClO 4<br />
-<br />
> I - > SCN -<br />
Cations; NH 4<br />
+<br />
> Rb + > K + > Na + > Li + > Mg 2+ > Ca 2+ > Ba2 +<br />
8
Chapter 1<br />
The ions at the start of the series are called kosmotropes or anti-chaotropes and have been<br />
reported to have stronger interactions with water molecules and promote hydrophobic<br />
interactions (40, 41). The effects of the salts on protein retention can also be explained by the<br />
number of water molecules released by the induction of different types of salts. The<br />
selectivity of the different salts can also be predicted by the differences in their capability to<br />
exclude water molecules from proteins and adsorbent surface (27).<br />
The mobile phase pH is another factor affecting the protein adsorption during hydrophobic<br />
interaction chromatography. At highly basic pH values (up to 9-10), a decrease in<br />
hydrophobic interactions between proteins and adsorbent occur, due to the changes in the<br />
hydrophilicity of proteins. In contrast, hydrophobic interactions increase by decreasing the pH<br />
of the mobile phase. The proteins which are usually not able to bind at neutral pH will have<br />
more chances of binding at acidic pH values (42). The basic proteins, such as lysozyme (pI =<br />
10.7) has shown longer retention at basic pH values. In contrast, acidic proteins, such as<br />
human serum albumin (pI = 5.2) has revealed less retention during chromatography at basic<br />
pH values (43). In another report, the total number of releasing water molecules were found<br />
higher, when the mobile phase pH was close to the pI of the protein and decreased when pH<br />
was away from the pI (44). The effect of pH has been rarely reported and the reason could be<br />
the instability of the proteins and adsorbents at elevated pH conditions. Another study also<br />
stated that an optimal pH should be maintained in HIC to obtain high purity and yield at their<br />
maximum extent (45-48). The temperature was also reported to have an effect in HIC. It has<br />
been observed that increasing the temperature promoted protein retention and lowering the<br />
temperature enhanced protein elution. At higher temperature, the proteins are usually<br />
denatured and hydrophobic residues come on surface which resulted in high hydrophobic<br />
interactions. Due to the higher chances of unfolding at elevated temperatures, the unstable<br />
proteins should be separated at low temperatures. The proteins purified in the cold room may<br />
not be reproducible at the room temperature (49, 50). All the reports for the effects of these<br />
9
Chapter 1<br />
factors were mostly studied with model proteins; however their applicability to a natural host<br />
still needs to be explored.<br />
1.4.2. Effect of stationary phases in HIC<br />
Stationary phases are usually composed of small hydrophobic groups (e.g. butyl, phenyl and<br />
hexyl) attached to the hydrophilic polymer base supports. The different types of stationary<br />
phases in HIC differ in the degree of ligand substitutions on the base support. The base<br />
supports can also differ in the chemical nature and particle size (51). The availability of the<br />
hydrophobic adsorbents is increasing in the market, due to the increasing applications of HIC<br />
as a purification method. The adsorbents determine the protein adsorption selectivity in HIC<br />
according to the nature of ligands and base supports. The straight chain alkyl ligands were<br />
reported for pure hydrophobic character while aryl ligands (phenyl) showed mixed mode<br />
behavior due to the presence of aromatic interactions in addition to the hydrophobic<br />
interactions (52-55). The polyether’s ligands such as polyethylene glycols and polypropylene<br />
glycols were also reported for their milder hydrophobic nature than alkyl ligands (56). The<br />
retention of cytochrome c, ribonuclease A, lysozyme and ovalbumin has been reported on<br />
polyvinyl alcohol, oligoamino alcohol, and polyoxyethylene silica supports in descending<br />
gradient of ammonium sulfate buffer. It has been observed that hydrophobic interactions<br />
between proteins and adsorbents increased with increasing chain lengths of the ligand (55, 57).<br />
However, resolution of the chromatography decreased when ligands of the higher chain<br />
lengths have been used (51). The effects of adsorbent hydrophobicity on the conformation of<br />
proteins were also reported. The elution profiles of lactalbumin have shown different behavior,<br />
when separated by polyether stationary phases having methyl and ethyl terminating groups.<br />
The lactalbumin has given one and two peaks for the adsorbents with methyl and ethyl<br />
ligands, respectively. The first peak was the native one while the second peak was the<br />
denatured peak of the lactalbumin. The second peak has been retained longer due to the<br />
10
Chapter 1<br />
exposure of hydrophobic residues usually buried in the interior of proteins. Other protein such<br />
as lysozyme when applied has shown single peak on both adsorbents due to the stable nature<br />
of the protein (46). The charged type HIC adsorbents have shown different behavior during<br />
chromatographic separations. Ionic interactions will occur between proteins and adsorbents in<br />
the presence of charges, which favor the use of charge free adsorbents in HIC. The choice of<br />
ligand type is hard to interpret and could be established case to case basis depending on the<br />
biomaterials to be separated (53, 54). The retention behavior of the proteins can be changed<br />
by adjusting the ligand substitution on the base support. The protein binding capacity has<br />
been increased by an increase in the substitution degree of the ligand. Once the degree of<br />
substitution reaches beyond the sufficient level, the binding capacity of proteins remains<br />
constant, although the strength of interaction increases. At very high substitution of the<br />
ligands, multipoint attachments occur between proteins and adsorbents resulting in stronger<br />
binding. Thus harsh conditions are required to elute the product which may also increase the<br />
chances of protein denaturation. Some studies have preferred low ligand density in case of the<br />
unstable proteins to preserve the native structures of proteins (58-60).<br />
There are different types of base supports such as hydrophilic polysaccharide gels (e.g.,<br />
agarose and dextran), synthetic polymers (polymethacrylate and polystyrene divinyl benzene)<br />
or modified silica gel commonly used in HIC. It has been recently investigated with model<br />
proteins, that the adsorbent chemistry and up to some extent its particle size have effects on<br />
the retention behavior during chromatography (61, 62). The total number of water molecules<br />
released upon adsorption was found proportional to the hydrophobic area of the adsorbent<br />
(44). All the above findings were based on the retention data of model proteins and have been<br />
never investigated with the process proteomics approach.<br />
11
Chapter 1<br />
1.4.3. Effect of protein properties in HIC<br />
There are several protein properties affecting the chromatographic separation of proteins in<br />
HIC. However, the most important is protein hydrophobicity (51) and specifically average<br />
surface hydrophobicity (63) which has a decisive role in HIC. The protein hydrophobicity can<br />
be either the hydrophobicity of the exposed amino acid residues of a protein called as<br />
“average surface hydrophobicity” or depending on the hydrophobicity of the exposed and<br />
buried residues of a protein called as “degree of hydrophobicity” (27). The protein<br />
hydrophobicity has the main role in HIC which relies on no absolute definition. There is a<br />
general agreement that protein hydrophobicity can be the combined hydrophobic<br />
contributions of all the amino acids in a protein sequence. The hydrophobic potential of the<br />
various proteins can be derived by utilizing several amino acid hydrophobicity scales. Several<br />
experimental and theoretical approaches have been used by different researchers to assign the<br />
hydrophobicity value to each individual amino acid (25, 31, 64, 65). There are several<br />
hydrophobicity scales available in open literature which can be used for the determination of<br />
protein hydrophobicity. Tanford is one of those scales based on the transfer of free energies of<br />
the amino acid side chains from an organic environment to an aqueous environment (25, 66).<br />
Proteins when applied in an aqueous solution, the hydrophilic amino acid residues such as<br />
serine, threonine, asparagines, and glutamine tend to be exposed on the surface while most of<br />
the hydrophobic amino acid residues such as alanine, valine, leucine, isoleucine,<br />
phenylalanine, tryptophan, and tyrosine will be buried in the interior of protein molecules.<br />
However, adsorption of proteins occurs based on binding of the surface hydrophobic patches<br />
of a protein on the adsorbent. Several methods have been reported to quantify “protein surface<br />
hydrophobicity”. The first one was based on the binding of fluorescence probes to the<br />
hydrophobic region of a protein which has given rise to an increase in fluorescence according<br />
to protein hydrophobicity. Due to the possibility of ionic interactions in case of the anionic<br />
probes, this methodology was considered as non-applicable (64). The second method was<br />
12
Chapter 1<br />
based on the surface hydrophobic residues in a protein three dimensional structure (67). In<br />
addition to protein hydrophobicity, there are other physicochemical properties of the proteins<br />
such as average flexibility, average polarity, molecular weight and charge which have been<br />
rarely used for their effects in HIC. The average flexibility was reported to have a direct<br />
relationship with retention time of the model proteins in HIC. The proteins with low<br />
flexibility have been reported to be rigid towards conformational changes during<br />
chromatographic separations. In contrast, the protein with high flexibility will have more<br />
flexible nature towards conformational changes and when they come in contact of HIC<br />
surface, it will unfold or expose the internal surface in order to increase hydrophobic<br />
interactions and result in longer retention during chromatography (33, 62). The average<br />
polarity of the proteins was rarely reported to have an effect on protein retention in HIC. It<br />
was reported before that there is no obvious relationship between retention time and<br />
isoelectric point of the individual proteins (68). The effect of molecular mass on the retention<br />
behavior of proteins has been investigated before in ion exchange chromatography (19).<br />
However, there are very few reports where the effect of protein molecular weight on the<br />
chromatographic separation has been observed in HIC (33). There are few reports available<br />
where the above mentioned properties have been studied in HIC. However, these properties<br />
have been never researched for their effects on the chromatographic separations of a host cell<br />
proteome during HIC.<br />
1.4.4. The comparison between HIC and RPC<br />
The protein adsorption on hydrophobic adsorbents is generally weaker than reported in<br />
reverse phase chromatography (RPC), although both methods were based on hydrophobic<br />
interactions. In RPC, the protein-adsorbent interaction is so strong that protein elution can<br />
only be exerted by using organic solvents as elution buffer. The use of organic solvents and<br />
strong hydrophobic interactions are usually resulting in irreversible denaturation of proteins.<br />
13
Chapter 1<br />
This is also detrimental to the native structure and biological activity of proteins (69). In<br />
contrast, HIC is based on mild hydrophobic interactions and have importance to maintain the<br />
native structures and biological activity of the target proteins. The protein retention time can<br />
be predicted in HIC based on the surface residues of a protein. However, due to the<br />
denaturation of proteins in RPC, the elution is based on the exposed and buried hydrophobic<br />
residues of a protein (70). In other words, the protein retention in RPC can be estimated from<br />
the primary structure of a protein (71). In Table 1, the comparison between HIC and RPC has<br />
been given based on some general features (72) . There are several reports where they have<br />
used RPC for the purification of peptides. Several models have been proposed for the<br />
prediction of peptide retention time (73, 74). Although RPC has been widely used in the<br />
purification of peptides, however the application of RPC to large polypeptides and proteins<br />
has seen a significant increase in the last decade. The prediction of protein retention time has<br />
been reported based on the hydrophobicity and polypeptide chain length (75). However,<br />
further improvement to design prediction models is highly needed to be investigated.<br />
Table 1: The comparison of HIC and RPC (Adapted from Ref. 72).<br />
Description HIC RPC<br />
Stationary phases Low ligand density High ligand denisty<br />
Hydrophobic conditions Mild hydrophobic conditions High hydrophobic conditions<br />
Elution buffer Decreasing gradient of salts Increasing gradient of<br />
organic solvents<br />
Conformational changes Low conformational changes High conformational changes<br />
Protein retention<br />
Based on exposed<br />
Based on exposed and buried<br />
hydrophobic residues<br />
hydrophobic residues<br />
14
Chapter 1<br />
1.5. Analysis of the yeast proteome as biotech feedstock<br />
The proteomics tools such as two dimensional gel electrophoresis (2-D PAGE) and MALDI-<br />
ToF-MS are becoming the prominent methods for the analysis of the cell proteomes of<br />
various organisms. The proteomics analysis is usually the electrophoretic separation of<br />
proteins using 2-D PAGE followed by protein identifications (106). These proteomics studies<br />
are important to design the purification strategies and bioprocesses for the recombinant<br />
proteins. The bioprocesses are usually step by step approaches such as protein extraction,<br />
concentration, chromatographic separations and polishing. Several bioprocesses have been<br />
developed using different host cells and chromatographic methods for the purification of<br />
target proteins (109-110). Previously, master step purification was reported for the separation<br />
of Haemophilus influenzae cell proteome by heparin chromatography followed by<br />
electrophoretic separations and identification of proteins (76). The master step purification<br />
has gained more interest due to complete genome sequencing of several microorganisms of<br />
industrial interest and with the additional new improvements in the 2-D PAGE and MALDI-<br />
ToF-MS. The studies of the host cell proteome expressing the recombinant proteins called<br />
expression systems are important for protein purification. The expression systems can have<br />
the advantage of producing the maximum amount of target proteins in comparison to natural<br />
systems. There are few expression systems for the recombinant protein production such as<br />
bacteria, plants, animals, and yeast cells, usually Saccharomyces cerevisiae and Picchia<br />
pastoris (77). The cell proteomes of several microorganisms such as Candida albican,<br />
Plasmodium falciparum and Haemophilus influenzae has been reported by 2-D PAGE<br />
technology (78, 112). The Saccharomyces cerevisiae was selected as a host cell proteome due<br />
to several advantages over other expression systems such as high cell growth, high protein<br />
expression ratio, post translational modifications and most importantly it was the first<br />
organism to have its genome completely sequenced. The crude extract of the yeast cell<br />
15
Chapter 1<br />
proteome has been studied before using 2-D PAGE and mass spectrometry (105). However,<br />
the yeast cell proteome has been rarely investigated for its separation behavior on<br />
hydrophobic adsorbents by HIC. Hence, a study was planned to get a deeper insight into S.<br />
cerevisiae cell proteome and its separation behavior during HIC. The yeast cell proteome was<br />
chromatographed on different hydrophobic adsorbents and the fractions were collected to<br />
further investigate the proteins available in each chromatographic fraction. In a previous<br />
report, a three dimensional protein characterization approach has been applied to a complex<br />
plant extract using aqueous two phase system instead of HIC (112). The separation of yeast<br />
cell proteome was also three dimensional (3D) based on hydrophobicity (HIC), followed by<br />
its separation based on isoelectric point and molecular weight (2-D PAGE). Thus, the<br />
chromatographic and electrophoretic separation of the proteome was mainly based on three<br />
basic properties of the proteins, such as molecular weight, isoelectric point and<br />
hydrophobicity.<br />
1.6. Mass spectrometry and database searching<br />
Mass spectrometry is an important step in most of the proteome wide approaches to<br />
understand the biological processes. During the last decade, the genomes of most of the<br />
organisms of biological importance have been sequenced for the better understanding of<br />
genomes and proteomes. Once the genome sequence information was collected, the paradigm<br />
has shifted from sequencing to identification. The current paradigm was to investigate the<br />
host cell proteomes through different proteomics tools such as 2-D PAGE and mass<br />
spectrometry. The poteins can be identified either directly from the gels or through gels free<br />
approaches from the mixtures in solution (Figure 4) (79). The tandem mass spectrometry and<br />
MALDI-ToF-MS can be used from gel free and gel based protein identifications, respectively.<br />
The gel based identification approach has been reported many times and have several<br />
advantages over gel free approach (106, 109, 111). The gel electrophoresis can remove low<br />
16
Chapter 1<br />
molecular weight impurities and buffer ions during electrophoresis for which the MALDI-<br />
ToF has more sensitivity. Another advantage of the gel based approach is that polyacrylamide<br />
materials act like safe containers to handle and excise very small amount of the proteins. The<br />
protocols for the gel based approach are well established in comparison to gel free approach<br />
(80, 81). The mass spectrometers consist of three essential parts such as ionizer, analyzer and<br />
detector. The ionization source converts molecules into gas-phase ions. This ionization is<br />
facilitated by the energy provided by laser. As ions exit the ion source, the individual mass to<br />
charge (m / z) ratios are separated by a second device, a mass analyzer. The mass analyzer<br />
separates ions on the basis of mass to charge ratios. In MALDI-TOF-MS, the molecules are<br />
given mass values based on their movement or time of flight in the drift tube. Lighter ions fly<br />
faster than heavier ions and when it reaches to the third part of the mass spectrometer called<br />
detector. The signals are recorded on the detector and a mass spectrum is obtained. The<br />
detector records the charge produced during the hitting of ions on the surface. The spectrum is<br />
produced based on mass charge values. The experimental masses can be then compared to the<br />
theoretical masses available in the proteome of specific organism (80). The peptide mass<br />
fingerprinting approach can be used for searching protein databases. The principle behind<br />
PMF is very simple; the identified protein is the protein has the best match between the<br />
experimental and theoretical mass data. A theoretical digest of all the proteins sequences in<br />
the database by a specific enzyme can be used for predicting the individual peptides. These<br />
theoretical peptides can be searched to match peptides derived from the experimental mass<br />
data and then ideally one protein has to match the protein of interest. The protein will be<br />
considered as identified if the score is greater than the score fixed by Mascot with a p value<br />
less than 0.05. In addition, the experimental and theoretical values of the molecular weight<br />
and isoelectric point of a protein must be in the similar range. The databases such as NCBI<br />
and Swiss Prot can be searched for the identifications of proteins (82).<br />
17
Chapter 1<br />
Figure 4: The schematic presentation of protein identification using mass spectrometry<br />
(Adapted from Ref. 79).<br />
1.7. The in silico Approach<br />
The term in silico is actually the computational models or simulations based on the<br />
experimental data which can be used to make hypothesis and to predict about any biological<br />
process. The in silico approach has been widely used in pharmacology to make predictions,<br />
suggest hypothesis and ultimately leads towards new findings in drug designing and<br />
therapeutics. The in silico approach have the capability to increase the chances of discovery<br />
and decrease the laborious work in the laboratory. Many discoveries in the pharmacology<br />
were made by using the in silico approaches (83, 84, 85). It was stated that successful<br />
industrial companies are those that collect information from their experiments and introduce<br />
some rules that can show the pathways to obtain the target product for the clinical analysis<br />
18
Chapter 1<br />
followed by its introduction into the market. It has been reported that in silico approaches can<br />
help to make decisions and simulate about certain pharmaceutical processes which can further<br />
reduce the space between pharmaceutical and engineering based disciplines (84). To develop<br />
the computational models, it is necessary to have enough experimental data in the databases.<br />
These computational models can be exploited to predict about any biological process. The in<br />
silico approach has been reported to play its role as a complement to in vitro and in vivo<br />
experimentation (83). The upstream and downstream processes need to be improved utilizing<br />
the in silico approaches. The advances in the upstream processes such as fermentation have<br />
been already achieved and a product can be produced in several grams instead of milligrams<br />
due to the introduction of genetic engineering technologies (85). However, the major<br />
bottleneck still exists in the downstream processing of bioproducts. The current lack of the<br />
accurate data about the bioseparation of the products and the structural implications of the<br />
bioproducts are the main hurdles to design the purification models. A downstream approach<br />
has been proposed to collect the experimental data to design models for the improvement of<br />
the in silico approach (Figure 5). To collect the experimental data using modern mass<br />
spectrometry based proteomics tools <strong>after</strong> crude fractionation are highly required to design<br />
efficient bioprocesses (86). However, the in silico approach has been rarely reported in the<br />
downstream processing of proteins and the reason could be the lack of information with the<br />
real expression systems. Mostly model proteins were used to produce data and that data was<br />
then used to predict about the retention behavior of the other model proteins using<br />
mathematical models. The next step was then to compare the experimental retention time with<br />
that of predicted ones. In case of deviations from the predicted one, certain parameters have to<br />
be changed accordingly to calibrate the computer model. Once the model has been calibrated,<br />
no further investigations are required and the researcher can simulate the separation behavior<br />
of proteins on computer.<br />
19
Chapter 1<br />
Figure 5: Flow chart describing the fractionation and characterization procedure of the crude<br />
extract for obtaining the data about bioprocess parameters. The data produced can be stored<br />
in databases to design the purification models (Adapted from Ref. 86).<br />
Several researchers have reported different methodologies to predict about the retention time<br />
of model proteins (15, 87). However, the prediction models were based on the data collected<br />
from very few experiments and due to this reason there is still no universal scheme which can<br />
be considered as the best one for the downstream processing. These models have several<br />
drawbacks which can raise questions on their applicability. A major drawback was that the<br />
experimental data for these models has been derived from the model proteins instead of a host<br />
cell proteome. Another major drawback was that the experimental data was based on the<br />
retention time and / or volume of the model proteins. This is not applicable to a cell proteome<br />
because at a certain time during chromatography, several proteins are going to be eluted<br />
instead of a single protein. The shortcomings in the previous in silico approaches have<br />
motivated our group to report a paradigm shift from focussing on the purification of a target<br />
20
Chapter 1<br />
protein towards purification of the main protein contaminants in a host cell proteome. The use<br />
of the cell proteome in relation with the chromatographic method will produce more authentic<br />
and realistic data and can be stored in databases. These databases can be used to generate the<br />
purification models for easy in silico downstream processing of proteins (19). Several<br />
conclusions have been made in that work although the approach was not complete in its<br />
essence. The mass spectrometry and computational methods were missing in that report and<br />
the data reported was not enough to design a model. In this work, a complete picture has been<br />
presented ranging from chromatographic fractionation of the yeast cell proteome to the<br />
characterization of proteins at molecular level. The proteins properties have been used to<br />
correlate proteins at the molecular level to their separation behavior during HIC.<br />
1.8. Goal of the work<br />
Complementing all the above discoveries made by several authors, the current work further<br />
progressed to a more comprehensive understanding of the hydrophobic adsorbents and their<br />
effects on the separation behavior of a cell proteome during HIC. The adsorbents are usually<br />
composed of ligands and base supports. The comparison of the adsorbents (different ligands)<br />
hydrophobicity has been reported before with model proteins. However, the adsorbents<br />
hydrophobicity has been never studied comparatively with a proteome wide approach. The<br />
reason could be the experimentally demanding and laborious approach which no one ventured<br />
to adopt for their studies. Therefore, this study was planned for the comparative analysis of<br />
the hydrophobic adsorbents as a function of yeast cell proteome and in relation to protein<br />
properties.<br />
Under the frame of the current research work, the following objectives were targeted.<br />
• To explore the influence of different hydrophobic ligands in HIC as a function of<br />
complex cell proteome.<br />
21
Chapter 1<br />
• To uncover for the first time the influence of different base supports in HIC with a<br />
proteome wide approach.<br />
• To explore the physicochemical properties of proteins for their correlation with<br />
chromatographic separation and ability to differentiate among different ligands and<br />
base supports.<br />
• To reconnoitre the contaminant profile of the yeast cell proteome <strong>after</strong><br />
chromatographic fractionation using 2-D PAGE, mass spectrometry and<br />
bioinformatics.<br />
• To collect the experimental data and define certain operational windows, which can be<br />
annotated for the future implementation of the in silico downstream processing.<br />
22
Chapter 2<br />
EXPERIMENTAL APPROACH
Chapter 2<br />
2. Experimental approach<br />
2.1. Materials<br />
Yeast extract, peptone, dextrose, analytical grade buffer salts was purchased from Applichem<br />
(Darmstadt, Germany). The protease inhibitor cocktail was from Sigma-Aldrich Chemie<br />
GmbH (Steinheim, Germany). Acrylamide and dithiothreitol (DTT) were purchased from<br />
Carl-Roth GmbH (Karlsruhe, Germany). The Bradford Protein Assay Kit was purchased from<br />
Pierce Biotechnology Inc. (Rockford, IL, USA). ColorPlus pre-stained protein marker (New<br />
England Biolabs, Frankfurt, Germany) and N, N, N, N-tetramethyl-ethylenediamine (TEMED)<br />
were obtained from Serva (Heidelberg, Germany). The Toyopearl ®<br />
adsorbents were<br />
purchased from Tosoh Biosciences GmbH (Stuttgart, Germany). The C10/10<br />
chromatography column, chromatographic materials Sepharose-Phenyl, Source-Phenyl and<br />
pre-cast gels for electrophoresis were purchased from GE healthcare Europe GmbH (Munich,<br />
Germany).<br />
2.2. Experimental strategy<br />
The experimental strategy has been presented in Figure 6, which started with the cultivation<br />
of yeast cells followed by cells disruption. The cell proteome was collected <strong>after</strong> cell<br />
disruption and subjected to chromatographic fractionation using several hydrophobic<br />
adsorbents. The chromatographic fractions were collected in triplicate. The two dimensional<br />
gels were performed for the 42 fractions obtained during chromatography. Several proteins<br />
from each gel were selected for identification by MALDI-ToF-MS. Computational tools were<br />
used to calculate several properties of the proteins. Finally, the physicochemical properties of<br />
the proteins were correlated with the chromatographic behavior during HIC. All these<br />
experimental steps have been explained sequentially in the following text.<br />
24
Chapter 2<br />
Yeast cell proteome and chromatographic fractionation by HIC<br />
Phenyl ligated media (3) Polymethacrylate based media (3)<br />
21 fractions (7 each) 21 fractions (7 each)<br />
Perform 2-D PAGE for<br />
21 fractions separately<br />
Perform 2-D PAGE for 21<br />
fractions separately<br />
Identification of certain<br />
proteins from each gel<br />
by MALDI-ToF-MS.<br />
Identification of certain proteins<br />
from each gel by MALDI-ToF-<br />
MS.<br />
Bioinformatics approach<br />
Calculate several protein properties from the primary structure<br />
Average surface hydrophobicity calculated from three dimensional<br />
structure using MATLAB and STRIDE as computational tools<br />
Correlate protein properties with the chromatographic behavior and investigate<br />
the effects of ligands and base supports during chromatography<br />
Figure 6: Experimental strategy of the overall work.<br />
25
Chapter 2<br />
2.2.1. Cell Cultivation<br />
S. cerevisiae pBIVU02-1 (Biomedal, Sevilla, Spain) strain was streaked onto an YPD agar<br />
plate (1% Yeast extract, 2% Peptone, 2% Dextrose, 2% Agar) such that isolated single<br />
colonies will grow. The plates were incubated for two days. Then 30 ml of YPD liquid media<br />
was inoculated in a 250 ml flask with a single colony of cells from the YPD agar plate and<br />
grow overnight at 30°C with vigorous shaking (220 rpm) in the YPD liquid medium, pH 7.5.<br />
Then pre-culture was used to inoculate 300 ml YPD liquid media (1% yeast extract, 2%<br />
peptone, 1%w/v dextrose, pH 5.8) in 1.0 L baffled flasks. Cultivation was performed at 30°C,<br />
220 rpm, and for 48 hours. The cells were harvested by centrifugation in the late exponential<br />
growth phase at 3220 g for 15 minutes. The resulting cell pellet was washed three times with<br />
20 mM sodium phosphate buffer (pH 7.5) before cell disruption.<br />
2.2.2. Preparation of the crude extract<br />
Cell disruption was performed by bead milling at 4°C utilizing 0.5 mm glass beads. The cell<br />
paste (30 g) was suspended in 60 ml 20 mM sodium phosphate buffer (pH 7.5) and contacted<br />
by the grinding media for 30 min. One hundred micro litres of protease inhibitor cocktail (P<br />
8849; Sigma, St. Louis, MO, USA) was added per gram of cells. Cellular debris was<br />
separated by centrifugation at 3220 g for 25 min. The supernatant was filtered employing a<br />
0.45 µm membrane (Ministart Sartorius, Gottingen, Germany). The lysate was then ready to<br />
be used as a sample for chromatography experiments.<br />
2.2.3. Chromatographic fractionation<br />
Chromatographic fractionation was performed in an AKTA FPLC system equipped with a<br />
Frac-900 fraction collector and UNICORN 4.10 software for data collection and analysis (GE<br />
healthcare Europe GmbH, Munich, Germany). Two sets of chromatography stationary phase<br />
materials were used; one set had three different base supports (Sepharose-Phenyl, Source-<br />
Phenyl and Toyopearl-Phenyl) harbored the same ligand and the other set had different<br />
26
Chapter 2<br />
ligands (Toyopearl-Hexyl, Toyopearl-Butyl and Toyopearl-Ether) with same base support.<br />
Bed volume was 5.0 ml; the quality of the packing was evaluated by residence time<br />
distribution analysis using 1% acetone as a tracer (88). Mobile phases were prepared as<br />
follows: buffer A: 20 mM sodium phosphate buffer pH 7.5 containing 1.6 M (NH 4 ) 2 SO 4 , and<br />
buffer B: 20 mM sodium phosphate buffer. Buffers were filtered and degassed prior to use.<br />
After equilibration in buffer A (10 CV), the cell lysate (10 ml; 8.0 mg/ml) was applied to the<br />
column. The sample was introduced by a 10 ml super loop (GE health care, Uppsala, Sweden).<br />
Non-retained materials were washed out in buffer A (5 CV) and applying a linear gradient (25<br />
CV) from 0% to 100% of buffer B exerted elution. The flow rate was 1 mL / min. Fractions<br />
were collected utilizing the following salt concentration values as cut-off: 1 (1.6 M), 2 (1.4<br />
M), 3 (1.0 M), 4 (0.8 M), 5 (0.6 M), 6 (0.2 M) and 7 (0 M). The chromatographic experiments<br />
were performed in triplicate and the fractions were pooled before analysis.<br />
2.2.4. Membrane dialysis<br />
The materials collected in each of the seven fractions were further desalted by membrane<br />
dialysis (Pierce Rockford, IL, United States). The membranes (7 KDa) with samples were<br />
stirred in 5 litres bottle full of water. The salt buffer ions have been removed from the samples<br />
by diffusion. The dialysis was performed for two days in a cold room (4°C) with a renewal of<br />
fresh water <strong>after</strong> each 12 hours interval. After desalting, the samples were concentrated under<br />
vacuum (Vacufuge concentrator 5301, Eppendorf AG, Hamburg, Germany).<br />
2.2.5. Two dimensional polyacrylamide gel electrophoresis (2-D PAGE)<br />
The chromatography fractions were obtained as described earlier and dissolved in Lysis<br />
buffer (7 M urea, 4% w/v CHAPS, 2 M thiourea, 2% v/v Pharmalyte TM 3-10, and 1% w/v<br />
DTT). The samples were then centrifuged (3220 g, 15°C) to remove any insoluble material.<br />
Isoelectric focussing was performed using 7 cm Immobiline TM Dry strips pH 3-10, kept at<br />
20°C in a flatbed Multiphor II unit (GE Healthcare Europe, GmbH, Munich, Germany). The<br />
27
Chapter 2<br />
IPG strips were kept for 16 hours in rehydration buffer (6 M urea, 2 M thiourea, 1% CHAPS,<br />
0.4% DTT, and 0.5% v/v pharmalyte TM 3-10). The sample 200 µl (65 µg proteins in 75 + 125<br />
µl of rehydration buffer) was applied in in-gel rehydration buffer instead of cup loading. The<br />
sample was allowed to absorb on a strip for 15 min, and then covered with silicon oil. After a<br />
sixteen hour rehydration period, focussing was performed. IEF was performed on these<br />
conditions: 50 V, 120 min. 150 V, 90 min. 500 V, 60 min, 1500 V, 40 min. 2500 V, 40 min<br />
and 3500 V, 180 min at 20°C. The minute amount of buffer ions present in the samples was<br />
removed by applying low voltage (100 volts) at the beginning for 5 hours and the filter paper<br />
beneath the electrode (where the salt ions have collected) was changed time by time. After<br />
focussing, the strips were first equilibrated for 15 minutes in 10 ml equilibration buffer (50<br />
mM Tris-HCl, pH 8.8, 6 M urea, 30% glycerol, 2% w/v SDS and 10 mg/ml DTT) containing<br />
20 µl bromophenol blue and subsequently for additional 15 minutes in the same buffer<br />
containing 25 mg/ml iodoacetamide instead of DTT. After equilibration, the strips were kept<br />
on top of a 12.5% SDS vertical slab and covered by 0.7% hot low melting point agarose in<br />
SDS electrophoresis running buffer (25 mM Tris, 192 mM glycine, 0.1% SDS). The sodium<br />
dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed in a Hoefer<br />
mini VE vertical electrophoresis system in 1 mm thick 10 cm × 10.5 cm gels, 25 mA per gel<br />
and 120 volts (19). Colloidal coomassie blue was used for staining the gels (89). The gels<br />
were first fixed for at least three hours in the fixing solution (50% ethanol and 3% phosphoric<br />
acid). The gels tend to shrink during fixing. Then the gels were washed in deionized water<br />
three times for 20 minutes at a time. After washing, the gels were preincubated for 1 hour in<br />
staining solution (34% methanol, 3% phosphoric acid and 17% w/v ammonium sulfate). Then<br />
a small amount of coomassie blue was added with a spatula tip to the gels and stained<br />
overnight. After staining, the gels were washed with deionized water to remove the<br />
background stain. The gels were scanned by 48 bit Epson color scanner (Epson perfection<br />
4990 Photo). 2-D gels of the chromatography fractions were performed in duplicate and the<br />
28
Chapter 2<br />
number of spots was counted based on size and isoelectric point values. Certain spots were<br />
selected for in-gel digestion and protein identification.<br />
2.2.6. Protein content of the fractions<br />
The protein concentration in the fractions was determined by the Bradford protein assay kit<br />
(Pierce, Rockford, IL) as per the manufacturer's instructions. The protein content of the crude<br />
extract was measured before using it for chromatographic experiments. The protein content<br />
was also measured in all the chromatographic fractions collected during HIC.<br />
2.2.7. Protein identification by MALDI-ToF-MS<br />
Certain protein spots were manually excised from the 2-D gel for the identification purposes.<br />
In-gel digestion was performed according to the Shevchenko´s protocol (90) with some<br />
modifications. A high amount of salt was present in the samples, so an additional step was<br />
followed where a wash solution (50% v/v methanol and 5% v/v acetic acid) was added to gel<br />
pieces and kept overnight. The washing step was repeated three times. The wash solution was<br />
very effective in removing all kinds of impurities hindering the identification process. The<br />
other additional step was the use of 0.5% TFA to dissolve the dried peptides instead of 0.1%<br />
TFA mentioned in Shevchenko´s protocol. The reason was the presence of minute amount of<br />
buffer ions in the samples which was negatively affecting the spotting of proteins on the<br />
MALDI target plate. After washing with 0.5% TFA, the spots on the plate were solid and<br />
resulted in mass spectra of high quality. The modifications in the existing protocol should be<br />
considered in the protein identifications specific in case when HIC is involved. The overall ingel<br />
digestion process was performed in seven steps such as washing, de-staining, reduction,<br />
alkylation, trypsin digestion, extraction and concentration of the peptide extract (Table 2).<br />
29
Chapter 2<br />
Table 2: The in-gel digestion protocol optimized specifically for the proteins fractionated by<br />
HIC<br />
Steps<br />
Description<br />
1. Washing the gels The gel pieces were washed overnight, with 50% (v/v) methanol<br />
and 5% (v/v) acetic acids for the removal of salts and other<br />
impurities.<br />
2. De-staining After washing, the gel pieces were incubated in 200 µl of 100<br />
mM ammonium carbonate/acetonitrile (1:1 v/v) for 30 minutes<br />
to destain the gels.<br />
3. Reduction After destaining, 50 µl of 10 mM DDT dissolved in 100 mM<br />
ammonium carbonate were added to gel pieces and incubated<br />
for 45 minutes at 56°C to reduce the disulfide bridges.<br />
4. Alkylation Then 100 µl of 55 mM iodoacetamide was added to samples and<br />
incubated for 20 minutes in the dark to inhibit the reformation of<br />
disulfide bonds and alkylate the free cysteins.<br />
5. Trypsin digestion Then 10 µl of trypsin and 90 µl of 25 mM ammonium<br />
bicarbonate in 9% acetonitrile solution were added to each<br />
sample and incubated in ice for 3 hours to absorb maximum<br />
enzymes. The samples were then incubated for 16 hours at 37°C<br />
in the digestion buffer for protein digestion.<br />
6. Extraction of<br />
peptides<br />
7. Concentration of<br />
the peptide extract<br />
a. 200 µl of Milli Q water was added to each sample and<br />
incubated for 15 minutes at 37°C to extract hydrophilic<br />
peptides.<br />
b. 200 µl of extraction buffer (1:2 v/v 5% formic<br />
acid/acetonitrile) was added to each sample and<br />
incubated for 15 minutes at 37°C to extract hydrophobic<br />
peptides.<br />
The peptide extract was concentrated and 15 µl of 0.5% v/v<br />
TFA was added to each sample, followed by centrifugation at<br />
10,000 rpm for 10 minutes. The samples were then ready for<br />
identification by MALDI-ToF-MS.<br />
After digestion, the sample was collected as supernatant and 3 µl of the sample was spotted<br />
on the MALDI target plate followed by 0.5% TFA for washing. The sample was air dried at<br />
room temperature. All mass spectra were calibrated using peptides from the auto digestion of<br />
trypsin. The peak lists were searched using the Mascot search engine<br />
30
Chapter 2<br />
(http://www.matrixscience.com) against NCBI and SwissProt databases. The search<br />
parameters were carbamidomethylation and methionine oxidation entered as variable<br />
modifications and 1 missed trypsin cleavage site. In all protein identifications, the scores were<br />
greater than the score fixed by Mascot as significant with a p value < 0.05 (91). The identified<br />
proteins were further characterized and analyzed with computational tools.<br />
2.2.8. Characterization of proteins utilizing Bioinformics<br />
The same procedure has been followed for the calculation of ASH as described by J.C.<br />
Salgado et al utilizing the Cowan-Whittaker scale (32, 92). Average surface hydrophobicity<br />
(ASH) of the proteins was calculated by writing a package in MATLAB, where it was<br />
mandatory to use the three-dimensional structure of a protein collected from protein databank<br />
(PDB, http://www.rcsb.org/pdb). STRIDE was used to generate a text file, which can<br />
determine the accessible surface area (ASA) of each amino acid at a particular position. After<br />
calculating the ASA, the ASH was measured by the package in MATLAB, programmed with<br />
the normalized values of Cowan-Whittaker hydrophobicity scale. According to this method,<br />
the ASH can be calculated by equation 1 as follows;<br />
Φ = ˆ rφ<br />
(1)<br />
suface<br />
∑<br />
i<br />
i∈A<br />
i<br />
Where<br />
Φ<br />
surface is the ASH of a protein, A is the collection of the 20 possible amino acids<br />
and ( φ i ) is the hydrophobicity of the amino acids of type i. The hydrophobicity of each amino<br />
acid was assigned according to Cowan-Whittaker scale. The fraction of superficial area ( r ∧ i)<br />
occupied by the amino acid i can be calculated by equation 2 as follows;<br />
31
Chapter 2<br />
∧<br />
r<br />
i<br />
=<br />
Si<br />
S<br />
∑<br />
jεA<br />
j<br />
(2)<br />
In equation (2),<br />
Si<br />
is the sum of the accessible surface area (ASA) for all the amino acids of<br />
type i. S j is the total surface area of the protein. The r ∧ i is the fraction of surface area occupied<br />
by amino acid i and A is the collection of 20 possible amino acids (93). The concept of this<br />
approach was based on the assumption that each amino acid on the protein surface contributes<br />
regarding its abundance, with the properties associated to the protein surface (32, 67).<br />
Average hydrophobicity (AH), average polarity, average bulkiness and average flexibility<br />
(AF) were calculated from the primary structure by using the scales available at Expasy<br />
ProtScale tool (94). The average hydrophobicity values were measured employing three<br />
different scales i.e. Tanford (T), Cowan-Whittaker (CW) and Miyazawa-Jernigan (MJ),<br />
respectively (25, 92, 95). The average bulkiness was calculated by the scale proposed by<br />
Zimmerman (96). The polarity values were calculated by adopting the Grantham’s and<br />
Zimmerman’s scales (96, 97). The flexibility indices introduced by Bhaskaran and<br />
Ponnuswamy were used to calculate the flexibility values of proteins (98).<br />
32
Chapter 3<br />
RESULTS AND DISCUSSION
Chapter 3<br />
3. Results and Discussion<br />
3.1. Influence of the different ligand chemistries during hydrophobic<br />
interaction chromatography<br />
3.1.1. Summary<br />
Several reports are available in literature in which various ligands have been compared based<br />
on the retention time of model proteins in hydrophobic interaction chromatography (HIC) (62,<br />
87, 99). However, the different ligands were comparatively investigated for the first time with<br />
the proteome wide approach. A proteome wide approach has direct industrial applicability and<br />
has several advantages over work performed with model proteins. This work is in the<br />
continuation of a previous report from our group, where the ion exchange beads were used to<br />
investigate the separation behavior of the insect cell proteome (19). Several conclusions were<br />
made in that report, although the approach was not complete in its essence. The mentioned<br />
approach was extended in this work for the comparative studies of the hydrophobic ligands<br />
employing some additional steps of mass spectrometry and bioinformatics; which were<br />
missing in the previous approach. This approach analyzed the chromatographic separation of<br />
the overall protein contaminants in the host cell proteome instead of focussing on any of the<br />
target protein. The Saccharomyces cerevisiae cell proteome was fractionated by different<br />
ligands during HIC. The chromatographic fractions were collected at specific salt<br />
concentrations, followed by their separation on two-dimensional polyacrylamide gel<br />
electrophoresis (2-D PAGE). Several spots were selected from each 2-D gel and subjected to<br />
identification by Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass<br />
Spectrometry (MALDI-ToF-MS). Later on, several protein properties such as average surface<br />
hydrophobicity, average hydrophobicity, average flexibility, average bulkiness and average<br />
polarity were calculated for the identified proteins using different bioinformatics tools. These<br />
properties were investigated for the first time with the proteome wide approach and correlated<br />
35
Chapter 3<br />
to the retention behaviors in HIC. These properties were also studied for their ability to<br />
differentiate among the three different ligands. In addition, this work was planned to collect<br />
the experimental data from the three ligands which can be used to design the purification<br />
model of recombinant proteins utilizing the in silico approach.<br />
3.1.2. Scientific background of the approach<br />
3.1.2.1. Hydrophobic adsorbents<br />
Hydrophobic interaction chromatography (HIC) is an important chromatographic technique<br />
used for the industrial scale purification of proteins. HIC was preferred over other techniques<br />
such as affinity chromatography, ion exchange chromatography and reverse phase<br />
chromatography, due to the mild hydrophobic interactions. These weaker interactions<br />
minimize the unfolding of the proteins and maintain the biological activities (68, 100). The<br />
hydrophobic adsorbents and protein properties are the two main parameters affecting the<br />
chromatographic behavior during HIC. The hydrophobic adsorbents are usually composed of<br />
hydrophobic ligands attached to the functional groups of the base supports. The hydrophobic<br />
ligands are usually ether, butyl, hexyl and phenyl groups depending on their chain lengths. As<br />
the chain lengths of the ligands are increasing, the water exclusion potential of the ligands is<br />
also increasing. In other words, the chain length of the ligand has a direct relationship to<br />
hydrophobic interactions. If the hydrophobic interactions between proteins and adsorbents are<br />
higher, the proteins will be tightly bound to the column and will elute at the end during<br />
chromatography. The hydrophobicity studies of the different ligands have been provided by<br />
the Vendor company and also reported in the open literature (62, 87, 99, 101). The ether<br />
ligand was reported to be the most hydrophilic and hexyl as the most hydrophobic ligand as<br />
presented in Figure 7 (99).<br />
Toyopearl-Ether < Toyopearl-Butyl < Toyopearl-Hexyl<br />
36
Chapter 3<br />
Ligand<br />
Base support<br />
(Toyopearl®)<br />
Figure 7: The chemistry and comparison of different ligands for their hydrophobicity based on<br />
model protein retention data (Adapted from Ref. 99).<br />
The ligands are usually attached to toyopearl ®<br />
base support by glycidyl ether coupling<br />
procedures. This procedure introduces a short spacer arm to minimize steric hindrance. At the<br />
start, HIC was a slow technique; rapid separation has been made possible by the introduction<br />
of micro particulate semi rigid supports (Toyopearl®) (101). This revealed the importance of<br />
toyopearl adsorbents at the industrial level. The physicochemical properties of the adsorbents<br />
are given in Table 3 (99). The three hydrophobic adsorbents of 65-100 µm bead sizes were<br />
selected for this study, which is the most suitable range for industrial scale purification. The<br />
hydrophobic charm of the ligands was narrated before with toyopearl as a common base<br />
support. However, the previous reports were based on the retention time of few model<br />
proteins and have been never studied with the biological mixtures. Hence, it was planned to<br />
37
Chapter 3<br />
investigate the hydrophobicity of the following three ligands as a function of the cell<br />
proteome and in relation to protein properties (Figure 8).<br />
Table 3: Physical properties of the adsorbents with different ligands (99)<br />
Adsorbents<br />
Base support<br />
Ligands<br />
Beads size<br />
(µm)<br />
Pore size<br />
(nm)<br />
DBC a (mg/mL)<br />
Toyopearl-<br />
Butyl<br />
polymethacrylate Butyl 100 100<br />
32<br />
Toyopearl-<br />
Hexyl<br />
polymethacrylate Hexyl 100 100<br />
33<br />
Toyopearl-<br />
Ether<br />
DBC a<br />
polymethacrylate Ether 65 100<br />
Dynamic binding capacity<br />
12<br />
Yeast proteome separation<br />
on hydrophobic adsorbents<br />
(HIC)<br />
Comparison of<br />
three different<br />
ligands<br />
Comparison of<br />
three base<br />
supports<br />
Protein<br />
characteristics<br />
Toyopearl-Hexyl<br />
Toyopearl-Butyl<br />
Toyopearl-Ether<br />
Source-Phenyl<br />
Sepharose-Phenyl<br />
Toyopearl-Phenyl<br />
Protein<br />
hydrophobicity and<br />
others<br />
Figure 8: The comparison of the three different ligands with common base support based on<br />
protein properties and as a function of the expression system.<br />
38
Chapter 3<br />
3.1.2.2. Protein properties in the context of HIC<br />
The physicochemical properties of the proteins such as average hydrophobicity, average<br />
polarity, average flexibility and average bulkiness can be determined by different scales<br />
proposed by several researchers (Table 4). These average properties were based on the<br />
exposed and buried amino acid residues of a protein. The average hydrophobicity is the<br />
average of the hydrophobicities of the exposed and buried amino acids of a protein. The<br />
average hydrophobicity of the proteins can be measured by the scales proposed by different<br />
researchers (25, 92, 95). Several experimental and theoretical approaches were used to<br />
determine the hydrophobicity indices for the individual amino acids. These methods have<br />
assigned different hydrophobicity values for each amino acid and can be used as different<br />
amino acid hydrophobicity scales. The scale of Cowan-Whittaker was derived from the amino<br />
acid retention times during chromatographic experiments by RP-HPLC (92). The scale of<br />
Miyazawa-Jernigan was based on the contact energies estimated from the protein tertiary<br />
structures. A direct relationship was observed between the contact energies of adjacent<br />
residues in the crystal structure and their corresponding hydrophobicities reported by Tanford<br />
(25, 95). The theoretical contact energies between amino acid residues in the crystal structures<br />
were used to assign the hydrophobicity value to each amino acid. The amino acid<br />
hydrophobicity scale proposed by Tanford was based on the amino acids solubility in water as<br />
well as in spontaneously increasing concentration of organic solvent (ethanol). The amino<br />
acids were classified as hydrophobic, intermediate and hydrophilic based on the free energy<br />
change by the transfer of amino acids from ethanol to water. Based on the data, a<br />
hydrophobicity scale was proposed for amino acid residues, where their free energy transfer<br />
values become more and more positive as the hydrophobic character of the protein increases<br />
(102). This revealed that hydrophobic amino acids will tend towards organic phase while<br />
hydrophilic amino acids will prefer to stay in the water. In other words, charged residues of<br />
the protein will tend towards the surface and in contrast, the hydrophobic residues will stay in<br />
39
Chapter 3<br />
the interior of the protein (25). The average hydrophobicity parameter was reported in<br />
literature for its relation to protein structure (66). However, this parameter was rarely<br />
investigated in context of the HIC. Average flexibility is another property which can be<br />
calculated by using Bhaskaran and Ponnuswamy’s scale. Average flexibility represents the<br />
average fluctuations of the all residues in a given protein. It was reported that proteins with<br />
high flexibility will have more chances to unfold and expose hydrophobic residues on the<br />
protein surface. This will increase hydrophobic interactions between proteins and adsorbents<br />
and result in longer retention of proteins during chromatographic experiments (33).<br />
Table 4: The physicochemical properties of the proteins investigated in this study<br />
Protein properties Scales Bioinformatic tools Source<br />
Average<br />
hydrophobicity<br />
Cowan-Whittaker’s<br />
scale,<br />
Expasy ProtScale tool Primary<br />
structure<br />
Miyazawa-Jernigan’s<br />
scale<br />
Tanford’s scale<br />
Average flexibility Bhaskaran and<br />
Ponnuswamy’s scale<br />
Expasy ProtScale tool Primary<br />
structure<br />
Average polarity Grantham’s scale,<br />
Zimmerman’s scale.<br />
Expasy ProtScale tool Primary<br />
structure<br />
Average bulkiness Zimmerman’s scale Expasy ProtScale tool Primary<br />
structure<br />
Average surface<br />
hydrophobicity<br />
Cowan-Whittaker’s<br />
scale<br />
Expasy ProtScale tool,<br />
MATLAB and STRIDE<br />
softwares<br />
Three<br />
dimensional<br />
structure<br />
The polarity scales proposed by Grantham and Zimmerman were used to quantify the polarity<br />
based on the protein sequence (96, 97). All the above mentioned properties were based on the<br />
primary structure of a protein. However, ASH was the most remarkable property in the<br />
40
Chapter 3<br />
context of HIC based on the exposed amino acid residues of a three dimensional protein<br />
structure. The ASH of a protein has a direct proportionality to the exposed hydrophobic<br />
surface area on the protein surface. The three crystal structures identified in this work have<br />
been compared for their hydrophobic surface residues and elution position during<br />
chromatography (Figure 9). The crystal structures of the enzymes were obtained using<br />
chimera as a visualization tool (http://www.cgl.ucsf.edu/chimera). The protein such as Serine<br />
threonine protein kinase (A) has very less hydrophobic surface residues in comparison to the<br />
protein Ubiquitin carboxyl terminal hydrolase 4 (B). Due to this reason, the protein A was<br />
eluted earlier than protein B during chromatography. In the same way, the protein B has less<br />
hydrophobic surface residues as compared to the protein Phosphogycerate mutase 1 (C). Due<br />
to the less hydrophobic residues, the protein B was eluted earlier than protein C. There are<br />
several other important enzymes identified at different elution ranges and can be analyzed at<br />
the molecular level for their hydrophobic content and corresponding chromatographic<br />
behavior. These surface hydrophobic residues can be quantified in terms of ASH from the<br />
three dimensional structure of a protein utilizing MATLAB and STRIDE as computational<br />
tools. ASH has been widely exploited to predict the retention times of model proteins during<br />
HIC (103). However, all the above mentioned properties have been never studied in relation<br />
to the host derived proteins. Therefore, the above mentioned properties were investigated for<br />
their correlation with the chromatographic behavior and potential to differentiate among the<br />
ligands. This work was planned to provide the data from a real bioprocess for designing the<br />
purification model of the recombinant proteins utilizing an in silico approach.<br />
41
Chapter 3<br />
A<br />
B<br />
C<br />
Figure 9: The crystal structures of three proteins identified in this work. These proteins were<br />
eluted at different positions during chromatography depending on the surface hydrophobic<br />
residues. The red and blue colors represent the hydrophobic and hydrophilic surface residues,<br />
respectively.<br />
42
Chapter 3<br />
3.1.2.3. Towards an in silico approach for the downstream processing of bioproducts<br />
The in silico approach is actually the computational models or simulations based on the<br />
experimental data which can be used for making hypothesis and predictions about any<br />
biological process (83, 84). This approach has been widely used in the pharmaceutical<br />
industry for drugs discovery as discussed in chapter 1. However, the in silico approach was<br />
rarely evaluated in practical terms for the downstream processing of proteins. Several research<br />
groups had proposed models for the prediction of the chromatographic behavior of proteins<br />
onto hydrophobic adsorbents with varying degrees of success but none of these approaches<br />
have gained a universal acceptance (26, 87, 103). The first and major criticism was that all the<br />
schemes were based on model proteins instead of real biological crude extract. The second<br />
criticism was that how can one predict about the separation of any specific protein from<br />
biological mixtures based on the data of few homologous model proteins and without<br />
considering the chromatographic fractionation of the host cell proteome. The third criticism<br />
was that those models were based on the retention time and / or volume of the model proteins<br />
and these two parameters have no applicability in terms of real bioprocess. At any specific<br />
space of time (minute), several proteins are supposed to be eluted instead of a single protein<br />
during chromatographic fractionation of a cell proteome. Instead of retention time and / or<br />
volume, the salt concentration should be specified for the protein elution under specific<br />
chromatographic conditions. They never tried a model based on the chromatographic<br />
separation of the total cell proteome as the approach is experimentally demanding and<br />
laborious. This approach has been adopted in this work as presented in Figure 10, where<br />
several techniques were employed ranging from chromatographic fractionation of cell<br />
proteome to identification and analysis of the proteins available in each chromatographic<br />
fraction. The identified proteins were characterized by several protein properties and these<br />
properties were then further correlated to the chromatographic behavior. The protein<br />
properties were also investigated for their ability to differentiate among the adsorbents. Aside<br />
43
Chapter 3<br />
of the comparative analysis of the ligands as a main ambition, this work was planned to<br />
collect the experimental data based on the chromatographic separation of a cell proteome. The<br />
information obtained regarding proteins elution ranges on different adsorbents and the<br />
corresponding protein properties can be used for designing the purification models of the<br />
recombinant proteins by an in silico approach. This work has given a first draft of the<br />
experimental data to design models and predict the elution position of the recombinant<br />
proteins during HIC through computer simulations.<br />
Figure 10: Schematic representation of the methodology towards an in silico approach.<br />
44
Chapter 3<br />
3.1.3. Results and Discussion<br />
3.1.3.1. Chromatographic profiles of different ligands<br />
The S. cerevisiae PBIVU02-1 was chosen as an expression system, because it was the first<br />
organism to have its genome completely sequenced. Scouting chromatographic experiments<br />
were performed in order to evaluate the separation behavior of the S. cerevisiae cell proteome<br />
on different ligands (Ether, Butyl and Hexyl) employing Toyopearl® as a common base<br />
support. A decreasing gradient of ammonium sulfate was performed from 1.6 to 0 M and<br />
seven fractions were collected at specific salt concentrations. The salt concentrations of the<br />
fractions have been specified by numbers such as 1 (1.6 M), 2 (1.4 M), 3 (1 M), 4 (0.8 M), 5<br />
(0.6 M), 6 (0.2 M) and 7 (0 M). These salt concentrations at elution positions were defined for<br />
the first time in order to investigate the proteins available in each chromatographic fraction.<br />
The prediction models reported in the previous studies were based on the retention time and /<br />
or volume of the model proteins instead of the salt concentrations at elution stage (62).<br />
However, the retention time and / or volume have no applicability for the fractionation of a<br />
cell proteome. Several proteins are supposed to be eluted at a fraction of time (minute), so to<br />
assign a specific elution time to any protein from a biological mixture is impractical.<br />
Therefore, the fractions were collected on specific salt concentrations during chromatography.<br />
The chromatograms revealed no clear matching in the separation profiles of the three<br />
adsorbents for the same crude extract (Figure 11). A high resolution was observed for<br />
Toyopearl-Ether- than -Butyl and -Hexyl. The reason could be the smaller bead size of<br />
Toyopearl-Ether. Another reason could be that resolution decreases when the chain length is<br />
increasing i.e. Toyopearl-Hexyl- and -Butyl have higher chain lengths than -Ether (51). These<br />
results confirmed that bead size and chain length of the ligands have a significant role in<br />
resolution. Overall resolution of chromatographic separation was very low and the reason can<br />
be the high amount of sample which was applied in this micro preparative chromatography.<br />
45
Chapter 3<br />
Figure 11: Represent the chromatographic profiles obtained <strong>after</strong> the fractionation of the cell proteome by HIC using Toyopearl-Hexyl, Toyopearl-<br />
Butyl and Toyopearl-Ether. Proteins were loaded in a mobile phase composed of 1.6 M (NH 4 ) 2 SO 4 at pH 7.5. The elution was exerted for 25 CV. The<br />
fractions were collected in 7 groups utilizing the following salt concentrations as cut off values: 1 (1.6 M), 2 (1.4 M), 3 (1.0 M), 4 (0.8 M), 5 (0.6 M), 6 (0.2<br />
M) and 7 (0.0 M). Each fraction was collected by pooling in triplicate. (- Refers to 280 nm mAU, --- is Buffer B %).<br />
46
Chapter 3<br />
The applied protein load (40 mg) for chromatographic experiments was within the range of<br />
dynamic binding capacities of the adsorbents. A high amount of proteins was adsorbed on<br />
Toyopearl-Hexyl- and -Butyl which confirmed the high dynamic binding capacities of these<br />
adsorbents. Toyopearl-Ether has shown less binding affinity and most of the proteins were<br />
lost in flow through as non retained components. The results revealed the binding affinities of<br />
the adsorbents for a cell proteome. In case of Toyopearl-Ether, almost 80% of the total load<br />
was lost during washing as evidenced by the Bradford method. However, for Toyopearl-Butyl<br />
and -Hexyl almost 50% of the proteins were lost in the flow through. The remaining half of<br />
the proteins were retained in the column and eluted according to their hydrophobicity during<br />
chromatography. A high amount of proteins was lost during flow through which confirmed<br />
the less hydrophobic nature of the S. cerevisiae cell proteome (104). The results suggested the<br />
use of high salt concentrations in order to increase the adsorption affinity of the yeast cell<br />
proteome during HIC.<br />
3.1.3.2. Two dimensional gel electrophoresis and proteins identification<br />
The two dimensional gels were performed for all the chromatography fractions collected<br />
within a specific operational window. Several proteins were selected from each gel for<br />
identification purposes; however in a few cases the tiny and weakly stained proteins were<br />
identified. Four out of the total identified proteins were visible on the gels with naked eyes,<br />
however were not visible when photographed and it is sometimes the case with weakly<br />
stained proteins (Figures 12-14). There are several reports where weakly stained proteins<br />
were identified, but the spots were not visible on the 2-D gel pictures (105-111). In total,<br />
ninety six proteins were identified using MALDI-ToF-MS, which revealed the applicability of<br />
the in-gel digestion method optimized in this work specifically for the proteins fractionated by<br />
HIC (Tables 5-7). The identified proteins can further provide the contaminant profile of the<br />
yeast cell proteome and is an addition to the proteomics technology. In the case of Toyopearl-<br />
47
Chapter 3<br />
Ether, some 2-D gels were observed to have the restricted number of protein spots which<br />
could be used as an advantage to have the targeted product on the specific conditions. Overall,<br />
three dimensional separation of the yeast cell proteome was performed, based on<br />
hydrophobicity (HIC), and then followed by isoelectric point and molecular weight (2-D<br />
PAGE). The three dimensional characterization can be a suitable method when choosing an<br />
efficient host for the protein purification (112). The chromatographic methods can be used in<br />
the prefractionation steps for proteomics studies. Several chromatographies have been used<br />
previously to decrease the dynamic range of the cell proteomes on 2-D gels such as heparin<br />
and hydroxyapatite used for the Haemophilus influenza and Escherichia coli, respectively (76,<br />
109). However, it was an initial effort to fractionate the yeast cell proteome by several<br />
hydrophobic adsorbents followed by its proteomics analysis.<br />
48
Chapter 3<br />
Figure 12: Represent 2-D D gels performed for the samples fractionated by Toyopearl-Ether.<br />
Each gel represents the specific fractions collected at different salt ranges such as A (1.6 M), B<br />
(1.4 M), C (1 M), D (0.8 M), E (0.6 M), F (0.2 M) and G (0.0 M). The spots indicated by numbers<br />
were selected for identification. The encircled spot has been explained in section 3.1.3.2.<br />
49
Chapter 3<br />
Figure 13: Represent 2-D D gels performed for the samples fractionated by Toyopearl-Butyl.<br />
Each gel represents the fractions collected at different salt ranges such as A (1.6 M), B (1.4 M),<br />
C (1 M), D (0.8 M), E (0.6 M), F (0.2 M) and G (0.0 M). The spots indicated by numbers were<br />
selected for identification. The encircled spot has been explained in section 3.1.3.2.<br />
50
Chapter 3<br />
Figure 14: Represent 2-D D gels performed for the samples fractionated by Toyopearl-Hexyl.<br />
Each gel represents the fractions collected at different salt ranges such as A (1.6 M), B (1.4 M),<br />
C (1 M), D (0.8 M), E (0.6 M), F (0.2 M) and G (0.0 M). The spots indicated by numbers were<br />
selected for identification. The encircled spots have been explained in section 3.1.3.2.<br />
51
Chapter 3<br />
Table 5. List of the identified proteins fractionated by Toyopearl-Ether and their characterization with different properties<br />
Prot A Salt B Prot ≠ C Prot. I.D D Mr E pI F PDB G ASH H AH I AH J AH K AP L AP M AF N AB O<br />
Low 1.6 A1-TE RAD16_YEAST 91 8.54 3AUX 0.4225 0.403 0.503 0.587 0.475 0.315 0.603 0.656<br />
A2-TE OYE2_YEAST 44 6.13 3RND 0.4234 0.390 0.524 0.606 0.465 0.274 0.612 0.638<br />
A3-TE Fox2p 96 8.96 3QRF 0.4289 0.363 0.484 0.582 0.507 0.318 0.588 0.637<br />
A4-TE YN92_YEAST 69 6.15 3OQ0 0.4319 0.365 0.491 0.585 0.503 0.310 0.591 0.671<br />
1.4 B1-TE PRX1_YEAST 29 8.97 2PWJ 0.4370 0.402 0.492 0.587 0.501 0.271 0.598 0.675<br />
B2-TE SYF1_YEAST 100 5.17 3GIX 0.4396 0.372 0.528 0.608 0.500 0.290 0.593 0.667<br />
B3-TE TPIS_YEAST 26 5.74 3TH6 0.4411 0.408 0.503 0.597 0.454 0.302 0.599 0.688<br />
B4-TE APM2_YEAST 69 6.41 1HES 0.4436 0.402 0.507 0.596 0.476 0.316 0.603 0.667<br />
1.0 C1-TE UPP1 24 5.58 2EHJ 0.4451 0.382 0.511 0.605 0.478 0.279 0.597 0.662<br />
C2-TE Fur1p 23 5.84 2JBH 0.4458 0.385 0.512 0.620 0.476 0.280 0.598 0.667<br />
C3-TE CAT8_YEAST 160 9.13 3SNP 0.4505 0.389 0.514 0.612 0.472 0.289 0.608 0.636<br />
C4-TE BUR1_YEAST 74 9.55 3R22 0.4495 0.390 0.511 0.614 0.478 0.284 0.624 0.649<br />
Medium 0.8 D1-TE TRM11_YEAST 50 7.64 1ZJR 0.4516 0.411 0.517 0.616 0.462 0.288 0.612 0.639<br />
D2-TE FSF1_YEAST 35 9.84 3OJ2 0.4584 0.412 0.518 0.617 0.461 0.279 0.614 0.648<br />
D3-TE YO316_YEAST 7 11.66 2Y6V 0.4612 0.414 0.520 0.619 0.443 0.277 0.643 0.627<br />
D4-TE IMH1_YEAST 105 5.52 3TCX 0.4618 0.415 0.526 0.620 0.457 0.275 0.645 0.634<br />
D5-TE FAF1_YEAST 38 9.33 3R3M 0.4620 0.406 0.528 0.621 0.455 0.274 0.614 0.698<br />
0.6 E1-TE ICP55_YEAST 57 6.11 1VZ2 0.4644 0.414 0.527 0.607 0.456 0.273 0.615 0.641<br />
E2-TE CORO_YEAST 72 5.64 2AQ5 0.4660 0.417 0.531 0.622 0.448 0.264 0.636 0.629<br />
E3-TE SPC2_YEAST 20 9.22 3SBT 0.4684 0.426 0.539 0.619 0.435 0.285 0.628 0.684<br />
E4-TE MSA2_YEAST 39 10.08 3POJ 0.4731 0.424 0.526 0.629 0.468 0.260 0.634 0.629<br />
E5-TE BOP3_YEAST 43 9.72 3PLT 0.4791 0.426 0.539 0.632 0.439 0.259 0.651 0.615<br />
High 0.2 F1-TE ATG23_YEAST 51 5.77 2ZPN 0.4705 0.422 0.534 0.628 0.443 0.259 0.631 0.676<br />
F2-TE ATG2_YEAST 178 5.62 2DYT 0.4771 0.427 0.531 0.622 0.454 0.261 0.637 0.652<br />
F3-TE RRS1_YEAST 22 9.46 2 HBL 0.4884 0.432 0.543 0.635 0.436 0.257 0.634 0.646<br />
F4-TE Fsf1p 31 9.84 3L06 0.4936 0.438 0.579 0.643 0.414 0.255 0.635 0.649<br />
F5-TE SPS1_YEAST 55 7.55 3CZ8 0.4987 0.436 0.547 0.638 0.454 0.252 0.635 0.660<br />
0 G1-TE SHU1_YEAST 171 7.88 3R7W 0.5065 0.438 0.570 0.637 0.405 0.213 0.636 0.616<br />
G2-TE SC61G_YEAST 8 9.48 2WW9 0.5119 0.445 0.543 0.620 0.445 0.248 0.642 0.670<br />
G3-TE PDE2_YEAST 60 6.14 1MCO 0.5187 0.449 0.547 0.622 0.421 0.242 0.643 0.675<br />
G4-TE NUP53_YEAST 52 9.53 3P3D 0.6023 0.448 0.572 0.645 0.415 0.262 0.644 0.613<br />
G5-TE COX1 Protein 90 9.67 3OMA 0.6336 0.439 0.543 0.604 0.435 0.270 0.647 0.655<br />
A ; Protein hydrophobicity, B ; Salt concentration, C ; Identity number, D ; I.D. in SwissProt database, E ; Molecular weight, F ; Isoelectric point, G ; I.D in<br />
PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales<br />
of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman,<br />
respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale.<br />
52
Chapter 3<br />
Table 6. List of the identified proteins fractionated by Toyopearl-Butyl and their characterization with different properties<br />
Prot A Salt B Prot ≠ C Prot. I.D D Mr E pI F PDB G ASH H AH I AH J AH K AP L AP M AF N AB O<br />
Low 1.6 A1-TB DBF20_YEAST 65 8.68 2LAH 0.3864 0.399 0.501 0.584 0.471 0.320 0.605 0.647<br />
A2-TB YP191_YEAST 41 5.55 3C9P 0.4087 0.415 0.440 0.603 0.475 0.301 0.596 0.649<br />
A3-TB SMA1_YEAST 28 9.85 3RDJ 0.4189 0.382 0.448 0.565 0.465 0.319 0.606 0.653<br />
A4-TB YSY6_YEAST 7 10.43 3NYB 0.4192 0.388 0.450 0.588 0.560 0.279 0.583 0.655<br />
1.4 B1-TB RDS2_YEAST 50 7.87 2KOA 0.4234 0.384 0.499 0.581 0.475 0.281 0.588 0.636<br />
B2-TB PABP_YEAST 64 5.71 2X4T 0.4244 0.371 0.499 0.590 0.489 0.277 0.592 0.613<br />
B3-TB FMP46_YEAST 15 9.38 2QL8 0.4442 0.404 0.498 0.593 0.485 0.278 0.594 0.671<br />
Medium<br />
B4-TB IDHH_YEAST 47 9.24 3BLX 0.4339 0.414 0.500 0.582 0.483 0.306 0.601 0.650<br />
1.0 C1-TB TAF5_YEAST 88 7.01 3HMH 0.4447 0.374 0.502 0.595 0.483 0.306 0.590 0.620<br />
C2-TB GFD1_YEAST 21 9.76 3LCN 0.4434 0.382 0.505 0.583 0.485 0.373 0.596 0.626<br />
C3-TB YDL052C 33 9.67 3R0S 0.4412 0.384 0.499 0.588 0.475 0.312 0.575 0.668<br />
C4-TB UBP4_YEAST 105 8.50 3IRT 0.4471 0.392 0.512 0.604 0.469 0.299 0.603 0.650<br />
0.8 D1-TB Vtil1p 10 4.82 3ONJ 0.4447 0.349 0.501 0.589 0.476 0.323 0.596 0.642<br />
D2-TB LDB7_YEAST 19 9.08 2R10 0.4457 0.388 0.507 0.594 0.466 0.291 0.598 0.605<br />
D3-TB TFC4p 120 5.14 3MA5 0.4573 0.408 0.495 0.592 0.478 0.288 0.595 0.678<br />
D4-TB DRS1_YEAST 85 5.42 3RC3 0.4593 0.405 0.512 0.602 0.465 0.285 0.602 0.626<br />
D5-TB TFC1_YEAST 73 5.23 2JO4 0.4619 0.408 0.506 0.605 0.481 0.283 0.603 0.651<br />
0.6 E1-TB HIM1_YEAST 47 9.15 3P82 0.4629 0.410 0.524 0.608 0.453 0.259 0.609 0.658<br />
E2-TB TEX1_YEAST 47 8.88 3MXJ 0.4630 0.414 0.528 0.615 0.450 0.256 0.607 0.635<br />
E3-TB FAF1_YEAST 38 9.33 3R3M 0.4620 0.405 0.526 0.609 0.456 0.275 0.613 0.585<br />
E4-TB TDH3p 35 6.46 1Q32 0.4647 0.405 0.534 0.607 0.450 0.255 0.608 0.627<br />
E5-TB RPN2_YEAST 104 5.85 2X5N 0.4712 0.417 0.534 0.637 0.449 0.258 0.610 0.639<br />
High 0.2 F1-TB ICP55_YEAST 57 6.11 1VZ2 0.4644 0.414 0.527 0.607 0.456 0.273 0.615 0.641<br />
F2-TB DSD1_YEAST 47 7.53 3ANU 0.4751 0.426 0.534 0.625 0.432 0.242 0.613 0.651<br />
F3-TB PLSC_YEAST 33 9.63 1IUQ 0.4770 0.446 0.535 0.635 0.431 0.235 0.615 0.670<br />
F4-TB YMX6_YEAST 105 9.72 2JXN 0.4826 0.429 0.542 0.622 0.422 0.256 0.680 0.585<br />
F5-TB MSA2_YEAST 39 10.08 3POJ 0.4731 0.412 0.526 0.615 0.468 0.268 0.634 0.629<br />
0 G1-TB RS7A_YEAST 21 9.83 3O2Z 0.4842 0.435 0.530 0.600 0.419 0.255 0.643 0.683<br />
G2-TB YSW1_YEAST 70 6.69 3OQC 0.5018 0.437 0.547 0.625 0.417 0.255 0.629 0.648<br />
G3-TB IF4A_YEAST 44 5.02 2G9N 0.5082 0.433 0.532 0.618 0.447 0.228 0.680 0.659<br />
G4-TB SGM1_YEAST 81 4.81 2YB7 0.5210 0.448 0.572 0.633 0.412 0.248 0.639 0.638<br />
G5-TB IMP2_YEAST 19 10.01 1L0N 0.5233 0.429 0.576 0.628 0.416 0.220 0.669 0.676<br />
A ; Protein hydrophobicity, B ; Salt concentration, C ; Identity number, D ; I.D. in SwissProt database, E ; Molecular weight, F ; Isoelectric point, G ; I.D in<br />
PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales<br />
of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman,<br />
respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale.<br />
53
Chapter 3<br />
Table 7. List of the identified proteins fractionated by Toyopearl-Hexyl and their characterization with different properties<br />
Prot A Salt B Prot ≠ C Prot. I.D D Mr E pI F PDB G ASH H AH I AH J AH K AP L AP M AF N AB O<br />
Low 1.6 A1-TH POP1_YEAST 100 9.83 1R5D 0.3694 0.400 0.515 0.580 0.461 0.291 0.606 0.644<br />
A2-TH MSS51_YEAST 50 8.84 315X 0.4013 0.327 0.465 0.604 0.551 0.280 0.573 0.662<br />
A3-TH Eno1_YEAST 46 6.16 3OTR 0.4095 0.375 0.472 0.555 0.547 0.387 0.588 0.614<br />
A4-TH Eno2_YEAST 46 5.67 2XSX 0.4130 0.376 0.476 0.558 0.542 0.372 0.579 0.613<br />
1.4 B1-TH RAD16_YEAST 91 8.54 3AUX 0.4225 0.403 0.478 0.587 0.475 0.315 0.586 0.656<br />
B2-TH YAE1_YEAST 16 5.22 2XE7 0.4268 0.377 0.493 0.600 0.488 0.338 0.604 0.639<br />
B3-TH DBf2_YEAST 66 8.86 3OQ0 0.4319 0.378 0.498 0.589 0.485 0.299 0.603 0.642<br />
Medium<br />
B4-TH Pho85 cyclin 34 9.65 2PK9 0.4274 0.380 0.500 0.603 0.484 0.309 0.602 0.670<br />
1.0 C1-TH AIM37_YEAST 26 9.39 21B7 0.4444 0.395 0.507 0.603 0.475 0.305 0.592 0.676<br />
C2-TH XRN1_YEAST 175 7.04 3PIE 0.4463 0.397 0.510 0.598 0.470 0.300 0.608 0.652<br />
C3-TH MCX1_YEAST 57 7.59 3P9D 0.4471 0.403 0.516 0.608 0.470 0.308 0.619 0.675<br />
C4-TH DEBYG2_YEAST 35 6.46 3STH 0.4345 0.405 0.527 0.612 0.462 0.287 0.595 0.627<br />
0.8 D1-TH TAF5_YEAST 88 7.01 3HMH 0.4447 0.406 0.502 0.595 0.483 0.273 0.597 0.620<br />
D2-TH YO316_YEAST 07 11.66 3PLU 0.4534 0.409 0.515 0.604 0.468 0.296 0.604 0.627<br />
D3-TH UB4_YEAST 105 8.50 3IRT 0.4471 0.410 0.509 0.593 0.479 0.290 0.603 0.650<br />
D4-TH YL345_YEAST 58 6.01 2DWO 0.4536 0.414 0.520 0.611 0.464 0.295 0.594 0.659<br />
D5-TH AI2M_YEAST 97 9.67 3IFJ 0.4567 0.418 0.526 0.612 0.463 0.289 0.594 0.657<br />
0.6 E1-TH R1R1_YEAST 99 5.83 2CVY 0.4581 0.420 0.520 0.613 0.460 0.287 0.592 0.639<br />
E2-TH YE119_YEAST 14 8.49 1M94 0.4594 0.426 0.524 0.613 0.458 0.271 0.629 0.652<br />
E3-TH YJHO_YEAST 104 5.29 3SDJ 0.4612 0.427 0.526 0.609 0.450 0.277 0.632 0.667<br />
E4-TH SNZ2_YEAST 32 5.40 1DTY 0.4631 0.429 0.527 0.605 0.445 0.270 0.645 0.648<br />
E5-TH METE_YEAST 85 6.07 3L7R 0.4660 0.430 0.534 0.621 0.452 0.276 0.644 0.655<br />
High 0.2 F1-TH NIC96_YEAST 96 6.37 2RFO 0.4672 0.431 0.526 0.608 0.456 0.276 0.645 0.662<br />
F2-TH YPD1_YEAST 19 4.58 1QSP 0.4697 0.432 0.530 0.612 0.450 0.270 0.618 0.656<br />
F3-TH BOP3_YEAST 43 9.72 3PLT 0.4791 0.425 0.539 0.618 0.425 0.259 0.651 0.696<br />
F4-TH SNF12_YEAST 63 5.47 2QLV 0.4711 0.434 0.542 0.618 0.447 0.284 0.646 0.644<br />
F5-TH DYR_YEAST 24 7.67 3Q1H 0.4764 0.438 0.536 0.621 0.440 0.289 0.647 0.662<br />
0 G1-TH SPS1_YEAST 55 7.55 3CZ8 0.4987 0.442 0.527 0.626 0.415 0.252 0.630 0.649<br />
G2-TH PNG1_YEAST 42 6.22 1PNG 0.4784 0.432 0.549 0.625 0.433 0.255 0.648 0.644<br />
G3-TH PMG1_YEAST 27 8.81 4PGM 0.4843 0.435 0.552 0.627 0.427 0.248 0.649 0.657<br />
G4-TH RL20A_YEAST 20 10.30 3O58 0.4896 0.437 0.554 0.630 0.419 0.243 0.649 0.676<br />
G5-TH MLP1_YEAST 21 5.14 2XMF 0.5159 0.442 0.553 0.634 0.418 0.237 0.650 0.647<br />
A ; Protein hydrophobicity, B ; Salt concentration, C ; Identity number, D ; I.D. in SwissProt database, E ; Molecular weight, F ; Isoelectric point, G ; I.D in<br />
PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales<br />
of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman,<br />
respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale.<br />
54
Chapter 3<br />
3.1.3.3. Influence of the ligands chemistries and average protein properties<br />
3.1.3.3.1. Average hydrophobicity (AH)<br />
Average hydrophobicity is actually the average of the hydrophobicities of the overall residues<br />
in the primary structure of a protein. Average hydrophobicity has been reported for its relation<br />
with the protein structure (66). However, average hydrophobicity has been rarely investigated<br />
in the context of HIC. There are different hydrophobicity scales proposed by several research<br />
groups but there is still a debate on which one scale is the best (32). Average hydrophobicity<br />
was calculated for the tabulated proteins on the scales proposed by Cowan-Whittaker,<br />
Miyazawa-Jernigan and Tanford (Tables 5-7) (25, 92, 95). A statistically significant linear<br />
correlation (r 2 = 0.82, p < 0.0001) was reported between the average hydrophobicity and<br />
protein retention behavior (Figures 15 A-C). This confirmed that protein with high average<br />
hydrophobicity will take longer time to elute during chromatography due to the high<br />
hydrophobic interactions between proteins and adsorbents. In contrast, the protein with less<br />
hydrophobic residues in its sequence will result in less hydrophobic interactions and<br />
ultimately the protein will elute at high salt concentrations in the beginning of the<br />
chromatography. It was reported before with model proteins, that in addition to a hydrophobic<br />
surface area, it is important to have a higher overall number of hydrophobic residues in the<br />
protein sequence for a strong binding on HIC adsorbents (62). The high abundance of<br />
hydrophilic amino acids in the contact surface area was also reported to have an inverse<br />
relationship with the protein retention (113). However, these results confirmed the role of<br />
average hydrophobicity in HIC with the process proteomics approach. The hydrophobicity<br />
value for the same protein on the scale of Miyazawa-Jernigan was differed with 1 and 2 digits<br />
to the scales of Cowan-Whittaker and Tanford, respectively. The reason for this could be the<br />
different direct and indirect approaches applied by researchers while determining the<br />
hydrophobicity indices for individual amino acids (25, 92, 95). The hydrophobicity indices of<br />
55
Chapter 3<br />
amino acids by Cowan-Whittaker’s scale were derived by a direct chromatographic method<br />
and due to this it was adapted for calculating ASH. The average hydrophobicity has given a<br />
direct relationship with the retention of protein on the mentioned scales. This confirmed the<br />
role of average hydrophobicity in the retention mechanism of HIC. The global averages of the<br />
r 2 values were 0.85, 0.82 and 0.79 for the scales of Miyazawa-Jernigan, Cowan-Whittaker and<br />
Tanford, respectively. The scales of the Cowan-Whittaker and Miyazawa-Jernigan have<br />
revealed high correlation coefficient values than Tanford. It was also reported before that the<br />
scales of Cowan-Whittaker and Miyazawa-Jernigan have provided the best correlation for the<br />
prediction purposes in comparison to other scales (114).<br />
Figure 15 A: Represents the average hydrophobicity calculated by the scale of Cowan-<br />
Whittaker. The protein average hydrophobicity values of the three ligands were correlated with<br />
the respective salt concentrations where the fractions were collected. The error bars represent<br />
the standard deviations of the average hydrophobicity values in each fraction. The statistical<br />
analysis revealed a significant correlation (r 2 = 0.82; p < 0.0001).<br />
56
Chapter 3<br />
Figure 15 B: Represents the average hydrophobicity calculated by the scale of Miyazawa-<br />
Jernigan. The protein average hydrophobicity values of the three ligands were correlated with<br />
the respective salt concentrations where the fractions were collected. The error bars represent<br />
the standard deviations of the average hydrophobicity values in each fraction. The statistical<br />
analysis revealed a significant correlation (r 2 = 0.85; p < 0.0001).<br />
57
Chapter 3<br />
Figure 15 C: Represents the average hydrophobicity calculated by the scale of Tanford. The<br />
protein average hydrophobicity values of the three ligands were correlated with the respective<br />
salt concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average hydrophobicity values in each fraction. The statistical analysis<br />
revealed a significant correlation (r 2 = 0.79; p < 0.0001).<br />
Some proteins were observed to have similar values of average hydrophobicity but differed<br />
with the average surface hydrophobicity (ASH). In this case, the protein with high ASH was<br />
retained longer than the one with less ASH, although they have the same average<br />
hydrophobicity. This revealed that ASH has a more decisive role in HIC than average<br />
hydrophobicity. Although ASH is the most precise method, but the trends observed in case of<br />
average hydrophobicity cannot be ignored. The reason for this could be that, although amino<br />
acids on the surface are deciding for retention, the unfolding of proteins may occur during<br />
chromatography experiments resulting in the retention based on the total hydrophobicity of a<br />
58
Chapter 3<br />
protein. It has been reported before that the retention of proteins in reverse phase<br />
chromatography (RPC) was dependent on the total hydrophobicity of a protein (70). This can<br />
give an additional advantage to average hydrophobicity and this parameter should also be<br />
explored in RPC. The trend lines of the three ligands were statistically non significant which<br />
revealed that average hydrophobicity have no ability to differentiate among the hydrophobic<br />
characters of the three ligands (Figure 15 A-C). The reason could be that average<br />
hydrophobicity was representing the total residues of a protein instead of only surface<br />
hydrophobic residues.<br />
3.1.3.3.2. Average polarity (AP)<br />
The average polarity of the proteins has been reported in the previous studies for its<br />
contribution in the three dimensional structure of a protein; however average polarity has been<br />
rarely explored in HIC (64, 115-117). The average polarity was calculated for the tabulated<br />
proteins utilizing the scales proposed by Grantham and Zimmerman (Tables 5-7) (96, 97).<br />
The polarity values for early and late eluted proteins were more than 0.49 (49%) and less than<br />
0.41 (41%), respectively on Grantham’s scale. The same trend was also observed on<br />
Zimmerman’s scale where early eluted proteins had a higher polarity 0.35 (> 35%) than the<br />
late eluted ones 0.24 (< 24%). The membrane proteins were reported to have less polarity (<<br />
40%) while soluble proteins have higher polarity, approximately 47%. The membrane<br />
proteins with the less polarity need detergents to be separated from respective membranes due<br />
to hydrophobic interactions. (117). It was also reported before with model proteins that a<br />
decrease in the polar surface area of the protein will increase the binding affinity on the<br />
adsorbents (62). However, the effect of polarity was examined for the first time in this work<br />
with the process proteomics approach. The average polarity of the proteins has revealed a<br />
statistically significant (r 2 = 0.82, p < 0.0001) inverse correlation with the protein retention<br />
during HIC (Figure 16 A-B).<br />
59
Chapter 3<br />
Figure 16 A: Represents the average polarity calculated by Grantham’s scale. The protein<br />
average polarity values of the three ligands were correlated with the respective salt<br />
concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average polarity values in each fraction. The statistical analysis revealed a<br />
significant correlation (r 2 = 0.89; p < 0.0001).<br />
60
Chapter 3<br />
Figure 16 B: Represents the average polarity calculated by Zimmerman’s scale. The protein<br />
average polarity values of the three ligands were correlated with the respective salt<br />
concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average polarity values in each fraction. The statistical analysis revealed a<br />
significant correlation (r 2 = 0.76; p < 0.0001).<br />
This revealed that proteins with high polar residues in the protein sequence were eluted at<br />
high salt concentrations at the beginning of the chromatography. In contrast, the proteins with<br />
less polar residues in the protein sequence were eluted at the end of the chromatography at<br />
low salt concentrations. The polar residues are usually the acidic and basic amino acids (Asp,<br />
Asn, Glu, Lys, Arg, Gln). These amino acid residues are highly polar and occur on the surface<br />
of protein at an aqueous interface. The non-polar residues such as (Ala, Val, Leu, Ile, Cys,<br />
Met, Pro, Phe, and Trp) are usually found in the interior portion of the proteins (117). Based<br />
on the abundance of these residues in a protein sequence, the polarity value has been assigned.<br />
61
Chapter 3<br />
The polarity value for the same protein on Grantham’s scale was almost one digit higher than<br />
Zimmerman’s scale due to the different approaches adopted by them. The r 2 values of the<br />
polarity with the protein retention were 0.89 and 0.76 for the scales of Grantham and<br />
Zimmerman, respectively. An inverse relationship between polarity and retention volume<br />
proved that proteins with less polarity will have a more non polar surface area and will result<br />
in stronger binding to HIC surface. In contrast, proteins with high polarity will have more<br />
polar and less non polar surface area, resulting in less binding to hydrophobic adsorbents. In<br />
other words, polarity has an inverse relationship with protein hydrophobicity. An inverse<br />
relationship was also observed between polarity and flexibility; the proteins with high polarity<br />
were less flexible and eluted at high salt concentrations. Although polarity has no decisive<br />
role in retention but still it can be considered as a contributing parameter during HIC. The<br />
average polarity has shown a correlation with the retention behavior of protein, but it has no<br />
potential to differentiate among the ligands. The reason can be that this property was derived<br />
from the primary structure of a protein instead of the exposed surface residues in the three<br />
dimensional structure. HIC has more relevance with the surface residues of a protein and is<br />
rarely affected by the total residues. Instead, RPC is a chromatographic method based on the<br />
total hydrophobic residues of a protein. Due to this reason, the average polarity might have<br />
the potential to differentiate the hydrophobic characters of the reverse phase chromatographic<br />
adsorbents.<br />
3.1.3.3.3. Average flexibility (AF)<br />
The average flexibility was calculated for the tabulated proteins using the Bhaskaran and<br />
Ponnuswamy’s scale (Tables 5-7) (98). The statistical analysis revealed a direct significant<br />
correlation (r 2 = 0.80, p < 0.0001) of average flexibility with the retention behavior of the<br />
proteins during chromatography (Figure 17). The direct relationship of flexibility with<br />
protein retention could be related to the abundance of cystein residues in the primary structure<br />
62
Chapter 3<br />
of a protein forming disulfide bridges. The protein with many cystein residues / or disulfide<br />
bridges will be less flexible towards conformational changes. In contrast, the protein with<br />
high flexibility will be more flexible towards conformational changes and when it come in<br />
contact of HIC surface, it will unfold or expose the internal surface in order to increase<br />
hydrophobic interactions with the adsorbent, consequently resulting in longer retention during<br />
chromatography (33, 62). The role of average flexibility in chromatographic separation has<br />
been confirmed here for the first time with the process proteomics approach. The direct effect<br />
of flexibility on the retention behavior has also revealed the direct relationship of flexibility<br />
with proteins hydrophobicity. If a protein has high hydrophobic residues in its sequence, the<br />
higher will be the flexibility and compressibility of that protein, which is in agreement of the<br />
previous paper (119). The proteins eluted at high salt concentrations at the beginning of the<br />
chromatography have less flexible nature than those proteins eluted at the end of the<br />
chromatography. The less flexible proteins have compact structures and hydrophobic parts of<br />
the proteins exist in the inner core and resulted in less hydrophobic interactions and earlier<br />
elution during chromatography. In contrast, the highly flexible proteins will unfold during<br />
chromatography and a hydrophobic part will come to the surface which will ensue high<br />
hydrophobic interactions and resulted in later elution during chromatography. Some proteins<br />
such as C4-TH and E4-TB were eluted earlier than D1-TH and F1-TB, respectively although<br />
they have almost identical ASH. This could be due to the higher flexibilities of the latter<br />
proteins resulting in longer retention during chromatography. The same behavior was also<br />
observed with model proteins where ribonuclease S has shown higher retention on Butyl-<br />
Sepharose than other proteins, although they have identical surface hydrophobicity. It was<br />
claimed that the higher flexibility of ribonulcease S might be favoring longer retention (63).<br />
63
Chapter 3<br />
Figure 17: Represents the average flexibility calculated by Bhaskaran and Ponnuswamy’s scale.<br />
The protein average flexibility values of the three ligands were correlated with the respective<br />
salt concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average flexibility values in each fraction. The statistical analysis revealed a<br />
significant correlation (r 2 = 0.80; p < 0.0001).<br />
The flexibility and stability of the proteins were also reported for their applications in nature,<br />
where the organisms stay alive at -50°C to over 100°C. All the organisms have the same 20<br />
amino acids; however it could be the combine contribution of the amino acid residues<br />
balancing the stability and flexibility of proteins and allowing the organism to stay on both<br />
temperature extremes (120). The average flexibility has shown no relation to the molecular<br />
weight and isoelectric point values of the proteins. In other words, the flexibility values of the<br />
tabulated proteins were independent of whether a protein is larger or smaller and acidic or<br />
basic in nature. The flexibility has revealed a direct relationship with the protein retention<br />
64
Chapter 3<br />
during chromatography; however it has no ability to differentiate among the ligands for their<br />
hydrophobic characters.<br />
3.1.3.3.4. Average Bulkiness (AB)<br />
The average bulkiness was calculated for the tabulated proteins employing the scale of<br />
Zimmerman (Tables 5-7). The average bulkiness has revealed no relationship (r 2 = 0.00; p =<br />
0.86) with the retention behavior of the proteins during chromatography (Figure 18). The<br />
average bulkiness values for the tabulated proteins were in a narrow range and the reason for<br />
this could be the size exclusion effect. Although bulkiness was expected to have an effect on<br />
chromatographic behavior due to its close relationship with the size of proteins, no effect was<br />
observed in the retention behavior. In the same way, the molecular weight values of the<br />
tabulated proteins have not revealed any correlation as they were representing the small<br />
fraction of the total proteins present on each 2-D gel. It was also researched by Zimmerman<br />
who proposed the scales for bulkiness and polarity, that there was no relationship between<br />
bulkiness and polarity values of the proteins according to his scales (96). In other words, if<br />
bulkiness has no correlation with polarity then it is supposed to have no relation with protein<br />
hydrophobicity. Our results confirmed that bulkiness has no role in the chromatographic<br />
separations during HIC.<br />
65
Chapter 3<br />
Figure 18: Represents the average bulkiness calculated by Zimmerman’s scale. The protein<br />
average bulkiness values of the three ligands were correlated with the respective salt<br />
concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average bulkiness values in each fraction. The statistical analysis revealed a<br />
non significant correlation (r 2 = 0.00; p = 0.86).<br />
3.1.3.3.5. Average surface hydrophobicity (ASH)<br />
The procedure as described by J.C. Salgado et al was used to compute the ASH values for the<br />
tabulated proteins utilizing the Cowan-Whittaker’s hydrophobicity scale (Tables 5-7) (32, 92).<br />
According to this scale, the hydrophobicity values were assigned to each amino acid residue<br />
based on the amino acid retention time in RP-HPLC. A statistically significant (r 2 = 90; p<br />
value < 0.006) direct relationship was reported between ASH and retention behavior on the<br />
respective adsorbents (Figure 19). The proteins with less ASH have less hydrophobic surface<br />
residues and hence eluted at the beginning of the chromatography in comparison to the<br />
66
Chapter 3<br />
proteins with medium or high ASH values. The proteins were classified into three groups such<br />
as low, medium or highly hydrophobic, depending on the ASH and the corresponding salt<br />
concentrations on which the proteins were eluted during chromatography. The proteins eluted<br />
at high salt concentrations in the beginning of the chromatography were grouped as less<br />
hydrophobic than those proteins eluted at the middle and / or at the end of the<br />
chromatography.<br />
Figure 19: Represents the average surface hydrophobicity calculated by Cowan-Whittaker’s<br />
scale. The protein average surface hydrophobicity values of the three ligands were correlated<br />
with the respective salt concentrations where the fractions were collected. The error bars<br />
represent the standard deviations of the average surface hydrophobicity values in each<br />
fraction. The statistical analysis on average revealed a significant correlation (r 2 = 0.90; p <<br />
0.006).<br />
The ASH values were 0.413, 0.458 and 0.522 for the proteins eluted at the beginning, middle<br />
and at the end of the chromatography, respectively. These ranges were in agreement of the<br />
67
Chapter 3<br />
published papers with model proteins (32, 62). This revealed that a protein with less ASH<br />
value retained for a shorter space of time than the protein with high ASH value. This kind of<br />
behavior was also reported by several research groups that binding affinity increased with the<br />
exposed hydrophobic residues of a protein. These results confirmed that hydrophobicity of the<br />
adsorbents and proteins have the combine contribution to increase the entropy of the system<br />
and resulted in hydrophobic interactions (118). The correlation coefficients (r 2 ) values of ASH<br />
with the respective salt concentrations were 0.95, 0.94 and 0.80 for Toyopearl-Hexyl-, -Butyl<br />
and -Ether, respectively. The r 2 values in the case of Toyopearl-Ether- was very less than -<br />
Butyl and -Hexyl. The reason can be the elution of the highly hydrophobic proteins at the end<br />
of the chromatography in the case of Toyopearl-Ether which exerted an effect on the overall<br />
trend line. Some proteins were reported to have high or similar ASH values than others but<br />
retained less i.e. C3-TH and E4-TB were eluted earlier than D3-TH and F1-TB, respectively.<br />
This revealed that higher the molecular mass of the protein, the longer it will stay on the<br />
adsorbent. The behavior based on protein molecular mass was not reported for Toyopearl-<br />
Ether due to its less hydrophobic nature which resulted in less ability to entrap larger proteins.<br />
Although ASH has been reported in the prediction of retention time of model proteins,<br />
however this parameter has been never studied in HIC as a function of the cell proteome (26,<br />
87). The salt and hydrophobicity ranges were defined for the first time in this work and can be<br />
used for the separation of target proteins employing HIC. ASH has revealed a direct<br />
relationship with the retention of proteins; however it has a limited ability to differentiate<br />
among different ligands.<br />
3.1.3.4. Comparative studies of the hydrophobic ligands based on average protein<br />
properties<br />
There are several reports where various ligands have been compared for their hydrophobicity<br />
utilizing the retention time of model proteins (22, 33, 61, 62, 99). However, the<br />
hydrophobicity of the ligands has been never explored with a cell proteome. The<br />
68
Chapter 3<br />
hydrophobicity of the ligands was investigated in this work with the yeast cell proteome.<br />
However, the comparison of ligands has not fully differentiated with the proteome wide<br />
approach (Figure 19). The reason could be the involvement of several protein properties<br />
affecting the retention behavior in HIC. Several protein properties have been demonstrated for<br />
their correlation with protein retention and their ability to differentiate among the<br />
hydrophobic ligands. The protein properties such as average surface hydrophobicity, average<br />
hydrophobicity, average flexibility and average polarity have shown a correlation with the<br />
chromatographic separation of the proteins. However, these properties may have less ability<br />
to differentiate among the ligands (Table 8).<br />
Table 8: Protein properties and their ability to differentiate among ligands and correlation with<br />
chromatographic behavior during HIC<br />
Protein properties<br />
Differentiation Correlation<br />
Average hydrophobicity × +<br />
Average polarity × -<br />
Average flexibility × +<br />
Average bulkiness × ×<br />
Average surface hydrophobicity Trend +<br />
+; indicates positive correlation, - ; indicates negative correlation; ×; indicates no correlation<br />
The results revealed that ligands cannot be fully differentiated based on protein properties and<br />
can only be estimated based on retention time of the model proteins. The reason could be that<br />
several proteins are supposed to be eluted at specific retention time during chromatographic<br />
separation of a cell proteome as a function of ligand chemistry. Another observation was that<br />
69
Chapter 3<br />
ligands revealed less difference at high salt concentrations than at low salt concentrations. In<br />
other words, the differences among the ligands were less at high hydrophobic conditions than<br />
at low hydrophobic conditions. This kind of behavior was also reported in our group with<br />
model proteins, where the differences among the adsorbents were higher at low salt<br />
concentrations than at the high salt concentrations (121). The reason for this could be that at<br />
the extreme hydrophobic conditions, the hydrophobic potential of the mobile phase has<br />
covered the potential of the ligands to demonstrate according to their hydrophobicities. At low<br />
salt concentrations, the mobile phase has low hydrophobicity and the adsorption was more<br />
dependent on the hydrophobicity of the ligands.<br />
3.1.3.5. Application note<br />
Several important proteins have been separated through chromatographic and electrophoretic<br />
separations and identified through MALDI-ToF-MS. This work can help those interested in<br />
the purification of proteins listed in Tables 5-7, with the methodology as described in chapter<br />
2. It will simplify the work of researchers and they will not have to scout for chromatographic<br />
experiments to figure out the elution position of any tabulated protein. Any of the target<br />
protein can be purified by isocratic chromatography, without going through trial and error<br />
chromatographies. Some of the important enzymes were Isocitrate dehydrogenase (eluted at<br />
1.4 M salt gradient on Toyopearl-Butyl), Phosphoglycerate mutase 1 (eluted at 0 M on<br />
Toyopearl-Hexyl), Triosephosphate isomerase (1.4 M on Toyopearl-Ether), Methyl<br />
transferase (0.6 M on Toyopearl-Hexyl), Enolase 1 and 2 (1.6 M on Toyopearl-Hexyl), D-<br />
serine dehydratase (0.2 M on Toyopearl-Butyl), Exoribonuclease 1 and Glyceraldehyde 3<br />
phosphate dehydrogenase (1.0 M on Toyopearl-Hexyl).<br />
70
Chapter 3<br />
3.1.4. Partial conclusions<br />
• The hydrophobicity of the three ligands has been investigated for the first time with<br />
the proteome wide approach utilizing different protein properties.<br />
• The ligands were not fully differentiated with the proteome wide approach, but a trend<br />
was observed.<br />
• Several protein properties such as average hydrophobicity, average flexibility, average<br />
polarity, average bulkiness and average surface hydrophobicity were investigated for<br />
their correlation with the protein retention during chromatography. Average polarity<br />
has shown an inverse relationship to the retention behavior of the proteins during HIC.<br />
Average surface hydrophobicity, average hydrophobicity and average flexibility have<br />
revealed direct relationship with the protein retention during chromatography.<br />
• Among all the properties, ASH was the only property which has a decisive role in HIC.<br />
• The large scale identification and characterization of the proteins has explored the<br />
contaminant profile of the yeast cell proteome and is an addition to the proteomics<br />
technology.<br />
• The data obtained by exploring the chromatographic behavior of the yeast cell<br />
proteome on the three different ligands can be used to design the purification models<br />
of recombinant proteins utilizing the in silico approach.<br />
71
Chapter 3<br />
3.2. Influence of the different base support chemistries during<br />
hydrophobic interaction chromatography<br />
3.2.1. Summary<br />
The comparison of the different base support chemistries as a function of the yeast cell<br />
proteome was explored for the first time in this work. This work was planned in the<br />
continuation of a previous report from our group where several ion exchange beads were used<br />
to investigate the separation behavior of the insect cell proteome instead of model proteins<br />
(19). Although several conclusions were made in that work, but still the approach was not<br />
complete in its essence and several techniques were missing. That approach was further<br />
progressed by exploring the different base supports in HIC employing some additional<br />
technologies. In this study, the Saccharomyces cerevisiae was selected as a host cell proteome<br />
due to the reasons such as its availability in protein data bank and it has the advantage of<br />
providing post translational modifications. An experimentally demanding and laborious<br />
approach was adopted ranging from chromatographic and electrophoretic separation of the<br />
cell proteome to identification and analysis of the proteins in each chromatographic fraction<br />
by mass spectrometry and bioinformatics tools. Moreover, certain protein properties were<br />
examined for their correlation to the chromatographic separation during HIC. These<br />
properties were also investigated for their ability to differentiate among the different base<br />
supports. In addition to the role of base supports chemistries, this work was planned to<br />
produce the data with the process proteomics approach which could be used to design the<br />
purification models for recombinant proteins utilizing the in silico approach.<br />
72
Chapter 3<br />
3.2.2. Chemistry of the base supports<br />
The adsorbents such as Toyopearl-Phenyl, Sepharose-Phenyl and Source-Phenyl were used<br />
with different base supports harbored phenyl as a common ligand. The physical properties of<br />
these adsorbents have been given in Table 9 as provided by the Vendor Company (99). The<br />
commercial adsorbents are usually composed of ligands and base supports of various nature.<br />
There are several base supports usually utilized in HIC such as Sepaharose ® , Toyopearl ® and<br />
Source ®<br />
that are composed of certain polymers in backbone chemistries i.e agarose,<br />
methacrylate and polystyrene. These polymers are usually of hydrophobic nature but were<br />
modified to the hydrophilic nature by coupling to certain ether and hydroxyl groups (99). The<br />
same was the case with polystyrene based adsorbents, however its chemistry has not reported<br />
yet. The chemistry of the three mentioned adsorbents has been shown in Table 10.<br />
Table 9: Physical properties of the adsorbents with different base support (99)<br />
Adsorbents Base supports Ligands Bead size<br />
(µm)<br />
Pore size<br />
(nm)<br />
a DBC<br />
(mg/mL)<br />
Toyopearl-<br />
Phenyl<br />
Sepharose-<br />
Phenyl<br />
Toyopearl ® Phenyl 100 100 40<br />
Sepharose ® Phenyl 34 65 35<br />
Source-Phenyl Source ® Phenyl 15 34 25<br />
a DBC; Dynamic binding capacity<br />
73
Chapter 3<br />
Table 10: The chemistry of base supports and ligands of the three adsorbents<br />
Base supports Ligand Adsorbents<br />
Polymethacrylate (Toyopearl®)<br />
Phenyl Ligand<br />
Toyopearl-Phenyl<br />
H CH 3 2<br />
C C<br />
O C<br />
O<br />
(HO) R<br />
H O 2<br />
C<br />
O<br />
C<br />
H 2<br />
C<br />
C<br />
CH 3<br />
H 2<br />
C<br />
C<br />
C O<br />
O<br />
R (OH)<br />
O<br />
C O<br />
C<br />
CH 3<br />
O<br />
HW-<br />
65<br />
O<br />
Agarose (Sepharose®)<br />
Phenyl Ligand<br />
Sepharose-Phenyl<br />
CH 2 OH<br />
OH O<br />
H<br />
H<br />
H<br />
O OH<br />
H<br />
O<br />
H<br />
O<br />
H OH<br />
O<br />
H<br />
CH 2<br />
H<br />
H<br />
O<br />
PS<br />
O<br />
H 2<br />
C<br />
OH<br />
C<br />
H<br />
H 2<br />
C<br />
O<br />
Polystyrene divinyl benzene (Source®)<br />
Phenyl Ligand<br />
Source-Phenyl<br />
O<br />
Not reported yet<br />
74
Chapter 3<br />
3.2.3. Results and Discussion<br />
3.2.3.1. Chromatographic fractionation<br />
Scouting experiments were performed to explore the most suitable operational conditions for<br />
the chromatographic separations of the yeast cell proteome. These standardized<br />
chromatographic conditions are summarized in Table 11. A decreasing gradient of<br />
ammonium sulfate was performed from 1.6 to 0 M and seven fractions were collected at<br />
specific salt concentrations in order to investigate the proteins available in each<br />
chromatographic fraction. The chromatograms in Figure 20 depict the resolution behavior of<br />
the three base supports for the same crude extract. There was no clear matching in the<br />
separation profiles of the three adsorbents due to the differences in their base support<br />
chemistries. The peaks with the number of 6, 4 and 1 correspond to Sepharose-Phenyl,<br />
Source-Phenyl and Toyopearl-Phenyl, respectively. A high resolution was observed in case of<br />
Sepharose-Phenyl and Source-Phenyl, due to their smaller bead size than Toyopearl-Phenyl.<br />
One large peak was observed for Toyopearl-Phenyl, the reason could be the larger bead size<br />
of the base support, which resulted in a low resolution during chromatographic separation.<br />
The overall resolution of the three chromatograms was not high. The reason could be the high<br />
amount of the sample load applied during chromatographic fractionation. In HIC, it is the<br />
initial effort to report the selectivity of phenyl media for a crude extract. The experimental<br />
approach applied in this work, where fractionation of a soluble cell proteome was done<br />
instead of model proteins will pave the way for a direct comparison between adsorbents in<br />
terms of hydrophobicity towards proteins derived from a complex biological mixture.<br />
Information of this kind of work has been rarely available in literature where they utilized a<br />
host cell proteome for chromatographic fractionation instead of the model proteins (63).<br />
A high amount of proteins (almost 50%) was lost in the flow through and the remaining<br />
proteins were retained in the column. This kind of behavior was also reported in Section 3.1,<br />
75
Chapter 3<br />
where approximately 50% of proteins were washed out during flow through. This revealed the<br />
high abundance of less hydrophobic proteins in the yeast cell proteome in comparison to the<br />
highly hydrophobic proteins. This also confirmed the less hydrophobic nature of the yeast cell<br />
proteome (104).<br />
Table 11: Standardized chromatographic conditions<br />
Feature<br />
Description<br />
Column volume<br />
5 mL<br />
Flow rate<br />
1 mL / min<br />
Equilibration buffer 10 CV a , 1.6 M, (NH 4 ) 2 SO 4 pH 7.5<br />
Sample load<br />
5 CV a , (8 mg / mL)<br />
Linear Gradient<br />
25 CV a<br />
Fraction volume<br />
3 mL<br />
CV a Column volume<br />
76
Chapter 3<br />
Figure 20: Represent the chromatographic profiles obtained <strong>after</strong> the proteome fractionation by HIC using Sepharose-Phenyl, Toyopearl-Phenyl and<br />
Source-Phenyl. Proteins were loaded in a mobile phase composed of equilibration buffer 1.6 M (NH 4 ) 2 SO 4 at pH 7.5. The elution was exerted for 25 CV.<br />
The fractions were collected in seven groups utilizing the following salt concentrations as cut off values: 1 (1.6 M), 2 (1.4 M), 3 (1.0 M), 4 (0.8 M), 5 (0.6<br />
M), 6 (0.2 M) and 7 (0.0 M). Each fraction was collected by pooling in triplicate. (- Refers to absorbance 280 nm, --- conductivity mS / cm).<br />
77
Chapter 3<br />
3.2.3.2. The electrophoretic separation and proteins identification<br />
The chromatographic fractions were further explored for their protein content by 2-D PAGE.<br />
The protein concentrations of the chromatographic fractions were kept similar during their<br />
electrophoretic separation by 2-D PAGE. This would give an insight to the total mass and the<br />
number of protein contaminants recovered in each chromatographic fraction (19). The 2-D<br />
gels were important for mapping the chromatographic separation of the yeast cell proteome<br />
and to excise certain spots for protein identifications (Figures 21-23). The seventy seven<br />
proteins were identified by MALDI-ToF-MS from all the gels representing specific<br />
chromatographic fractions (Tables 12-14). The four proteins were visible on the gels with the<br />
naked eyes, however were not visible when photographed and it is sometimes the case with<br />
the weakly stained proteins (105-111). The proteins identified from each chromatographic<br />
fraction were collected at different salt concentrations and can be separated through isocratic<br />
chromatography. This will avoid trial and error chromatographies to figure out the elution<br />
position of any of the tabulated protein. The large scale identification and characterization of<br />
the proteins has explored the contaminant profile of the yeast cell proteome and is an addition<br />
to the proteomics technology. The identified proteins and the corresponding 2-D gels can also<br />
be used in future to identify proteins by relying on 2-D gel electrophoresis image databases<br />
(123). The previous work on HIC was limited to a few homologous model proteins and<br />
average surface hydrophobicity parameter, which may not portray a global picture of the<br />
retention mechanism during HIC (23, 62). However, the global analysis of the base supports<br />
and protein properties as a function of complex cell proteome has overwhelmed the<br />
shortcomings in the previous approaches.<br />
78
Chapter 3<br />
Figure 21: Represent 2-D D gels performed for the samples collected by Toyopearl-Phenyl. Each<br />
gel represents the fractions collected at different salt concentrations such as A (1.6 M), B (1.4<br />
M), C (1 M), D (0.8 M), E (0.6 M), F (0.2 M) and G (0.0 M). The spots indicated by arrows were<br />
selected for identifications.<br />
79
Chapter 3<br />
Figure 22: Represent 2-D D gels performed for the samples fractionated by Sepharose-Phenyl.<br />
Each gel represents the fractions collected at different salt concentrations such as A (1.6 M), B<br />
(1.4 M), C (1 M), D (0.8 M), E (0.6 M), F (0.2 M) and G (0.0 M). The spots indicated icated by arrows were<br />
selected for identifications. . The encircled spots have been explained in section 3.2.3.2.<br />
80
Chapter 3<br />
Figure 23: Represent 2-D D gels performed for the samples fractionated by Source-Phenyl. Each<br />
gel represents the fractions collected at different salt concentrations such as A (1.6 M), B (1.4<br />
M), C (1 M), D (0.8 M), E (0.6 M), F (0.2 M), G (0.0 M). The spots indicated by arrows were<br />
selected for identifications. . The encircled spots have been explained in section 3.2.3.2.<br />
81
Chapter 3<br />
Table 12. List of the identified proteins fractionated by Toyopearl-Phenyl and their characterization with different properties<br />
Prot A Salt B Prot ≠ C Prot. I.D D Mr E pI F PDB G ASH H AH I AH J AH K AP L AP M AF N AB O<br />
Low 1.6 A1-TP KIN4_YEAST 90 9.50 2LAH 0.3864 0.356 0.486 0.570 0.495 0.303 0.540 0.622<br />
Medium<br />
A2-TP SLU7P 41 8.57 2HBP 0.3901 0.319 0.419 0.604 0.464 0.365 0.591 0.613<br />
A3-TP YDR532cp 44 4.80 1RW7 0.4015 0.375 0.488 0.605 0.496 0.326 0.611 0.668<br />
A4-TP ENO2_YEAST 46 5.67 2XHO 0.4198 0.401 0.467 0.608 0.494 0.298 0.579 0.613<br />
1.4 B1-TP HIBCH_YEAST 56 8.57 3BPT 0.4351 0.398 0.496 0.609 0.509 0.321 0.584 0.651<br />
B2-TP YKL215Cp 49 8.11 1K1D 0.4375 0.403 0.507 0.611 0.526 0.293 0.587 0.597<br />
B3-TP PMG1_YEAST 27 8.81 3R7A 0.4427 0.385 0.520 0.608 0.467 0.297 0.608 0.644<br />
B4-TP DENR_YEAST 22 8.78 2WPV 0.4449 0.394 0.523 0.609 0.473 0.295 0.615 0.644<br />
1.0 C1-TP UPT 28 6.75 2EHJ 0.4451 0.408 0.528 0.606 0.467 0.288 0.607 0.669<br />
C2-TP SPC2_YEAST 20 9.22 3PSI 0.4445 0.415 0.539 0.612 0.485 0.285 0.573 0.684<br />
C3-TP SEC4_YEAST 23 6.62 1G16 0.4471 0.411 0.522 0.607 0.465 0.286 0.604 0.624<br />
C4-TP PCL2_YEAST 35 9.72 2PMI 0.4478 0.400 0.523 0.613 0.464 0.288 0.616 0.668<br />
0.8 D1-TP PES4_YEAST 70 9.48 2V6Z 0.4609 0.404 0.531 0.622 0.478 0.278 0.613 0.648<br />
D2-TP PGM2_YEAST 63 6.18 3OLP 0.4624 0.404 0.532 0.621 0.453 0.264 0.612 0.625<br />
D3-TP YKL084W 13 6.42 3QWP 0.4672 0.418 0.535 0.618 0.456 0.280 0.625 0.659<br />
0.6 E1-TP ADH1_YEAST 36 6.21 3MEQ 0.4643 0.426 0.560 0.633 0.457 0.267 0.639 0.604<br />
E2-TP LOC1_YEAST 23 10.31 3O2Z 0.4842 0.429 0.542 0.624 0.464 0.252 0.629 0.634<br />
E3-TP DSD1_YEAST 47 7.53 3GWQ 0.4857 0.426 0.547 0.625 0.462 0.242 0.644 0.611<br />
E4-TP YPT52_YEAST 26 4.49 3RSB 0.4713 0.428 0.565 0.630 0.453 0.265 0.628 0.621<br />
High 0.2 F1-TP RTG2_YEAST 65 8.17 3OYC 0.4871 0.422 0.536 0.625 0.440 0.262 0.617 0.637<br />
F2-TP VATE_YEAST 26 5.33 2KZ9 0.4843 0.438 0.578 0.629 0.455 0.270 0.628 0.658<br />
F3-TP IDHH_YEAST 47 9.29 2RNG 0.4938 0.434 0.542 0.634 0.454 0.256 0.641 0.650<br />
F4-TP COQ3_YEAST 36 6.51 2YBO 0.5010 0.441 0.533 0.630 0.445 0.270 0.645 0.664<br />
0 G1-TP LONM_YEAST 127 5.43 2X36 0.5109 0.447 0.625 0.640 0.440 0.264 0.649 0.639<br />
G2-TP AIM4_YEAST 14 10.31 3RLS 0.5120 0.445 0.612 0.638 0.442 0.253 0.679 0.613<br />
G3-TP SPS1_YEAST 55 7.55 3CM1 0.5340 0.451 0.585 0.656 0.444 0.242 0.674 0.660<br />
G4-TP RL16B_YEAST 22 10.54 3R3T 0.5488 0.489 0.619 0.638 0.432 0.241 0.681 0.649<br />
A ; Protein hydrophobicity, B ; Salt concentration, C ; Identity number, D ; I.D. in SwissProt database, E ; Molecular weight, F ; Isoelectric point, G ; I.D in<br />
PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales<br />
of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman,<br />
respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale.<br />
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Chapter 3<br />
Table 13. List of the identified proteins fractionated by Sepharose-Phenyl and their characterization with different properties.<br />
Prot A Salt B Prot≠ C Prot. I.D D Mr E pI F PDB G ASH H AH I AH J AH K AP L AP M AF N AB O<br />
Low 1.6 A1-PS KIN4_YEAST 90 9.50 2LAH 0.3864 0.356 0.486 0.570 0.495 0.303 0.540 0.622<br />
Medium<br />
A2-PS ORF194 21 8.50 2X5T 0.4184 0.384 0.497 0.594 0.490 0.321 0.604 0.628<br />
A3-PS YJM789 93 6.05 3A15 0.4245 0.334 0.448 0.566 0.532 0.343 0.579 0.604<br />
A4-PS RPIA_YEAST 28 5.63 3KWM 0.4351 0.408 0.526 0.625 0.465 0.288 0.584 0.630<br />
1.4 B1-PS YHR042Wp 33 8.45 2XNC 0.4194 0.366 0.462 0.559 0.507 0.348 0.602 0.648<br />
B2-PS NDUS6_YEAST 12 9.37 3RHD 0.4202 0.405 0.520 0.600 0.462 0.289 0.608 0.605<br />
B3-PS BIG1_YEAST 39 5.20 3LTL 0.4231 0.368 0.542 0.586 0.440 0.326 0.610 0.694<br />
B4-PS LEUC_YEAST 85 5.61 3Q68 0.4342 0.401 0.492 0.578 0.465 0.285 0.612 0.626<br />
1.0 C1-PS ICP55_YEAST 57 6.11 2XPG 0.4281 0.383 0.495 0.607 0.456 0.273 0.615 0.641<br />
C2-PS PRS7_YEAST 51 5.32 3BPT 0.4351 0.383 0.496 0.594 0.487 0.324 0.612 0.623<br />
C3-PS MSC3_YEAST 80 9.14 2WGP 0.4522 0.401 0.518 0.579 0.517 0.280 0.612 0.601<br />
C4-PS RE114_YEAST 48 9.26 2PMI 0.4478 0.377 0.506 0.578 0.481 0.253 0.629 0.655<br />
0.8 D1-PS PNPP_YEAST 34 6.00 1VJR 0.4473 0.414 0.547 0.621 0.428 0.243 0.601 0.635<br />
D2-PS CDC73_YEAST 44 7.72 3OIP 0.4499 0.415 0.509 0.599 0.476 0.284 0.620 0.641<br />
D3-PS YER145c-a-yeast 16 9.51 3HIE 0.4515 0.418 0.539 0.624 0.454 0.274 0.609 0.653<br />
0.6 E1-PS RS3A1_YEAST 28 10 2KHJ 0.4535 0.415 0.547 0.586 0.482 0.258 0.608 0.649<br />
E2-PS KPYK1_YEAST 54 8.00 1A3X 0.4583 0.411 0.537 0.624 0.449 0.264 0.616 0.645<br />
E3-PS UB4_YEAST 105 8.44 2V6Z 0.4609 0.411 0.539 0.605 0.454 0.249 0.612 0.651<br />
E4-PS VATC_YEAST 44 6.22 1U7l 0.4615 0.415 0.534 0.621 0.451 0.240 0.623 0.667<br />
High 0.2 F1-PS GPN_YEAST 39 4.43 1A4R 0.4647 0.432 0.628 0.628 0.454 0.237 0.624 0.654<br />
F2-PS METE_YEAST 85 6.07 3EG3 0.4757 0.410 0.534 0.621 0.452 0.234 0.632 0.655<br />
F3-PS ADH1_YEAST 36 6.21 3MEQ 0.4643 0.426 0.560 0.633 0.426 0.227 0.639 0.604<br />
0 G1-PS CEX1_YEAST 84 5.27 1Z3H 0.4795 0.418 0.540 0.621 0.446 0.242 0.617 0.655<br />
G2-PS DPM1_YEAST 30 7.70 2GEJ 0.4997 0.452 0.572 0.635 0.411 0.245 0.640 0.666<br />
G3-PS 54S L22 34 9.98 3R3T 0.5488 0.449 0.660 0.655 0.412 0.298 0.639 0.635<br />
G4-PS MCM3_YEAST 107 5.26 3F9V 0.5853 0.455 0.628 0.603 0.410 0.214 0.624 0.628<br />
A ; Protein hydrophobicity, B ; Salt concentration, C ; Identity number, D ; I.D. in SwissProt database, E ; Molecular weight, F ; Isoelectric point, G ; I.D in<br />
PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales<br />
of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman,<br />
respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale.<br />
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Chapter 3<br />
Table 14. List of the identified proteins fractionated by Source-Phenyl and their characterization with different properties<br />
Prot A Salt B Prot ≠ C Prot. I.D D Mr E pI F PDB G ASH H AH I AH J AH K AP L AP M AF N AB O<br />
Low 1.6 A1-SP ENT4_YEAST 28 9.38 2QY7 0.3715 0.374 0.484 0.566 0.493 0.314 0.594 0.638<br />
Medium<br />
A2-SP SLU7P 41 8.57 2HBP 0.3901 0.334 0.419 0.604 0.464 0.365 0.591 0.613<br />
A3-SP RS6_YEAST 26 10.44 2XZM 0.4068 0.366 0.477 0.552 0.498 0.373 0.583 0.637<br />
A4-SP RM32_YEAST 21 9.86 3BYI 0.4174 0.373 0.492 0.562 0.512 0.358 0.612 0.653<br />
1.4 B1-SP YN93_YEAST 32 4.97 2PKP 0.4169 0.366 0.475 0.548 0.501 0.348 0.574 0.663<br />
B2-SP LRG1_YEAST 116 8.54 3BYI 0.4174 0.361 0.490 0.611 0.443 0.269 0.585 0.664<br />
B3-SP TDXH_AERPE 28 6.32 3A5W 0.4175 0.374 0.484 0.580 0.493 0.314 0.587 0.665<br />
B4-SP VID22_YEAST 102 5.06 3IFW 0.4232 0.385 0.488 0.619 0.446 0.314 0.603 0.635<br />
1.0 C1-SP BIG1_YEAST 39 5.20 3LTL 0.4231 0.403 0.498 0.586 0.440 0.326 0.610 0.694<br />
C2-SP VAM6_YEAST 97 6.61 1KMD 0.4255 0.386 0.524 0.564 0.452 0.302 0.589 0.674<br />
C3-SP SKG3_YEAST 114 8.37 3MP8 0.4324 0.392 0.498 0.605 0.486 0.305 0.631 0.616<br />
C4-SP Cha1p 22 9.70 3O05 0.4375 0.398 0.506 0.605 0.472 0.302 0.601 0.631<br />
0.8 D1-SP SYFA_YEAST 57 5.53 2WP1 0.4441 0.410 0.521 0.610 0.464 0.291 0.591 0.653<br />
D2-SP PWP2_YEAST 103 4.94 2Y9D 0.4442 0.415 0.518 0.617 0.464 0.275 0.604 0.635<br />
D3-SP YH139_YEAST 11 9.75 2YBA 0.4500 0.408 0.537 0.611 0.446 0.280 0.590 0.658<br />
0.6 E1-SP YKL085W 35 8.46 1MLD 0.4493 0.409 0.546 0.617 0.451 0.255 0.604 0.631<br />
E2-SP 1pk1p 11 6.96 2XAM 0.4537 0.410 0.545 0.623 0.443 0.304 0.634 0.697<br />
E3-SP MTC1_YEAST 53 4.50 3KOX 0.4576 0.415 0.572 0.619 0.418 0.269 0.657 0.651<br />
High 0.2 F1-SP YDL186Wp 32 5.46 2J04 0.4619 0.424 0.590 0.651 0.428 0.246 0.643 0.625<br />
F2-SP RDL2_YEAST 16 9.65 3L7S 0.4699 0.431 0.532 0.615 0.424 0.294 0.660 0.658<br />
0 G1-SP PAP_YEAST 64 7.95 3C66 0.4771 0.427 0.633 0.611 0.418 0.294 0.668 0.662<br />
G2-SP VPS64_YEAST 67 6.97 3R42 0.4927 0.423 0.598 0.651 0.426 0.283 0.639 0.628<br />
G3-SP BRE2 58 5.78 2QIY 0.4797 0.453 0.633 0.648 0.427 0.226 0.634 0.631<br />
G4-SP SPP41 99 8.93 2XF5 0.5263 0.464 0.617 0.653 0.410 0.219 0.651 0.629<br />
A ; Protein hydrophobicity, B ; Salt concentration, C ; Identity number, D ; I.D. in SwissProt database, E ; Molecular weight, F ; Isoelectric point, G ; I.D in<br />
PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales<br />
of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman,<br />
respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale.<br />
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Chapter 3<br />
3.2.3.3. Influence of the base support chemistries and average protein properties<br />
3.2.3.3.1. Average hydrophobicity (AH)<br />
Average hydrophobicity is the classical parameter based on the overall hydrophobic residues<br />
of a protein. It was reported before that in addition to hydrophobic surface residues, it is<br />
important to have a higher overall number of hydrophobic residues in the protein sequence for<br />
the stronger binding to the HIC adsorbents (62). However, the average hydrophobicity has<br />
been never investigated with the process proteomics approach. Hence, the three different<br />
hydrophobicity scales were used to calculate average hydrophobicity from the primary<br />
structures of the tabulated proteins (Tables 12-14). A statistically significant (r 2 =0.86, p <<br />
0.0001) linear relationship was reported between the average hydrophobicity and protein<br />
retention behavior on different base supports (Figure 24 A-C). In some cases, the proteins<br />
were reported to have similar average hydrophobicity values but differed with average surface<br />
hydrophobicity. This revealed that two proteins can have the same amount of total<br />
hydrophobic residues in the primary sequence but can differ with surface hydrophobic<br />
residues. The average hydrophobicity values for some proteins were similar; however their<br />
elution position was different from each other. In this case, the proteins with high ASH<br />
retained longer than the one with low ASH, although they have similar average<br />
hydrophobicity. The proteins such as B3-TP, and F1-TP retained longer during<br />
chromatography than A4-TP and E3-TP, respectively. These results confirmed that ASH has a<br />
decisive role in protein retention during chromatography. The overall values of average<br />
hydrophobicity revealed that the scale of Miyazawa-Jernigan was differed with 1 and 2 digits<br />
to the scales of Cowan-Whittaker and Tanford, respectively. The reason for this difference<br />
could be the various direct and indirect approaches, while determining the hydrophobicity<br />
indices for individual amino acids (25, 92, 95). The mentioned scales were calculated with<br />
quite different approaches and often correlated very poorly (124). The global averages of the<br />
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Chapter 3<br />
r 2 values from the data of the three base supports were 0.90, 0.89 and 0.79 for the scales of<br />
Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. It was also reported before<br />
that the scales of Cowan-Whittaker and Miyazawa-Jernigan have provided the best correlation<br />
for the prediction purposes in comparison to other scales (114). Although ASH is the most<br />
precise method, but the trends observed in case of average hydrophobicity cannot be ignored.<br />
The reason for this could be that, although amino acids on the surface are deciding for<br />
retention, the unfolding of proteins may occur during chromatographic experiments resulting<br />
in retention based on the total hydrophobicity of a protein.<br />
Figure 24 A: Represents the average hydrophobicity calculated by the scale of Cowan-<br />
Whittaker. The protein average hydrophobicity values of the three base supports were<br />
correlated with the respective salt concentrations where the fractions were collected. The error<br />
bars represent the standard deviations of the average hydrophobicity values in each fraction.<br />
The statistical analysis revealed a significant correlation (r 2 = 0.89; p < 0.0001).<br />
86
Chapter 3<br />
Figure 24 B: Represents the average hydrophobicity calculated by the scale of Miyazawa-<br />
Jernigan. The protein average hydrophobicity values of the three base supports were<br />
correlated with the respective salt concentrations where the fractions were collected. The error<br />
bars represent the standard deviations of the average hydrophobicity values in each fraction.<br />
The statistical analysis revealed a significant correlation (r 2 = 0.90; p < 0.0001).<br />
87
Chapter 3<br />
Figure 24 C: Represents the average hydrophobicity calculated by the scale of Tanford. The<br />
protein average hydrophobicity values of the three base supports were correlated with the<br />
respective salt concentrations where the fractions were collected. The error bars represent the<br />
standard deviations of the average hydrophobicity values in each fraction. The statistical<br />
analysis revealed a significant correlation (r 2 = 0.79; p < 0.0001).<br />
It was reported that the folded proteins have less non polar surface area in comparison to the<br />
unfolded ones (125). This confirmed that during unfolding, the hydrophobic residues of the<br />
proteins were exposed on the surface and resulted in longer retention during chromatography.<br />
It was also described in the mentioned report that there has been no relation to the non-polar<br />
surface area and the size of protein. In the same way, no relation was observed here between<br />
the size and hydrophobicity of the proteins. It has been reported that there were very few<br />
buried residues which have zero accessible surface area. Most of the protein interior was<br />
composed of amino acids have contact with the solvent (125). This also revealed that most of<br />
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Chapter 3<br />
the amino acids in a protein had a contribution in the retention behavior and due to this reason,<br />
the correlation of average hydrophobicity with retention seem sensible. The average<br />
hydrophobicity has been also reported for its significance in reverse phase chromatography<br />
(RPC). The RPC is based on the overall hydrophobicity derived from the primary structure<br />
instead of only surface residues of a protein (126). This revealed that average hydrophobicity<br />
and average surface hydrophobicity have decisive roles in HIC and RPC, respectively. The<br />
analysis of the two methods of protein hydrophobicity confirmed that ASH was more relevant<br />
to HIC than average hydrophobcity in terms of chromatographic separations. The trend lines<br />
(Figure 24 A-C) revealed that although average hydrophobicity has a role in HIC, but it has<br />
no ability to differentiate among the hydrophobic characters of the three base supports.<br />
3.2.3.3.2. Average polarity (AP)<br />
The average polarity was for the first time investigated for its correlation with protein<br />
retention behavior in HIC as a function of the cell proteome. The average polarity was<br />
calculated for the proteins using the scales proposed by Grantham and Zimmerman (Tables<br />
12-14) (96, 97). A statistically significant (r 2 = 0.82, p < 0.0001) inverse relationship was<br />
observed between polarity and retention behavior of the proteins on different base supports.<br />
The proteins eluted at high salt concentrations were highly polar than the proteins eluted at<br />
low salt concentrations. In other words, the proteins eluted at the beginning of the<br />
chromatography were more polar than those proteins eluted at the end of the chromatography.<br />
The average polarity values of the Grantham’s scale were higher in early eluted proteins (><br />
0.49) than late eluted proteins (< 0.41). The same trend was also observed on Zimmerman’s<br />
scale, where early eluted proteins had high average polarity (> 0.35) than late eluted proteins<br />
(< 0.24). The reason can be that proteins with high polar residues retained for a shorter space<br />
of time than those with low polar residues. In a previous study, it has been mentioned that<br />
membrane proteins had low polarity below 40% while soluble proteins had higher polarity<br />
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Chapter 3<br />
approximately 47%. The membrane proteins with the low polarity need detergents to be<br />
separated from respective membranes due to hydrophobic interactions (117). However, in the<br />
same study the polarity has not been correlated with the protein retention behavior in HIC.<br />
The polarity values for the same protein on Grantham’s scale were almost one digit higher<br />
than Zimmerman’s scale. This could be due to the different approaches used by the two<br />
researchers while calculating the polarity indices for individual amino acids. It has been<br />
reported that polar residues are normally located in the outer part of the protein while nonpolar<br />
residues in the inner portion (117). The polarity of a protein has a direct relationship<br />
with the number of polar residues (Asp, Asn, Glu, Lys, Arg and Gln) in the protein sequence.<br />
An inverse relationship was reported between polarity and retention behavior of the proteins<br />
(Figure 25 A-B). This revealed that the proteins with less polarity have a more non-polar<br />
surface area and resulted in stronger binding to the HIC surface. In contrast, proteins with<br />
high polarity had more polar and less non-polar surface area, resulting in less binding to<br />
hydrophobic adsorbents and earlier elution during chromatography. This also ensured that<br />
polarity has an inverse relationship with protein hydrophobicity.<br />
90
Chapter 3<br />
Figure 25 A: Represents the average polarity calculated by Grantham’s scale. The protein<br />
average polarity values of the three base supports were correlated with the respective salt<br />
concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average polarity values in each fraction. The statistical analysis revealed a<br />
significant correlation (r 2 = 0.84; p < 0.0001).<br />
91
Chapter 3<br />
Figure 25 B: Represents the average polarity calculated by Zimmerman’s scale. The protein<br />
average polarity values of the three base supports were correlated with the respective salt<br />
concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average polarity values in each fraction. The statistical analysis revealed a<br />
significant correlation (r 2 = 0.80; p < 0.0001).<br />
The r 2 values of the polarity with the retention of the proteins were 0.84 and 0.80 for the<br />
scales of Grantham and Zimmerman, respectively. An indirect relationship was also observed<br />
between polarity and flexibility of the proteins. The proteins with high polarity were less<br />
flexible and eluted at the beginning of the chromatography. These results confirmed the effect<br />
of average polarity in the chromatographic separation of proteins with the process proteomics<br />
approach. The polarity of the surface amino acids can also be used as an alternative parameter<br />
to average surface hydrophobicity and can be named as average surface polarity (ASP). The<br />
ASP parameter can be calculated for the tabulated proteins in the same way as ASH was<br />
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Chapter 3<br />
calculated. The ASP parameter can be used for the prediction of protein retention time in HIC.<br />
The average polarity has revealed no differences among the base supports for their<br />
hydrophobicity.<br />
3.2.3.3.3. Average surface hydrophobicity (ASH)<br />
There is no universally accepted method to be considered for the measurement of protein<br />
hydrophobicity; however it can be estimated through several hydrophobicity scales proposed<br />
by different co-workers (25, 92, 95). The hydrophobicity scale proposed by Cowan-Whittaker<br />
(92) was used to calculate ASH. This scale was based on a direct method and was derived<br />
from the amino acid retention time in RP-HPLC. The approach of J.C. Salgado et al (32, 92)<br />
was used for calculating ASH utilizing the bioinformatics tools. This property was<br />
demonstrated by averaging the hydrophobic potentials of the surface amino acid residues<br />
present in the three dimensional structure of a protein. All the amino acids on the surface of a<br />
protein have contributed in the ASH; however the eight non polar hydrophobic amino acids<br />
such as alanine, leucine, isoleucine, tryptophan, methionine, phenylalanine and proline have<br />
the major contributions. The ASH values have been calculated for the tabulated proteins<br />
(Tables 12-14). The statistical analysis revealed a significant direct correlation (r 2 = 0.91, p <<br />
0.002) between ASH and protein retention behavior on the respective adsorbents (Figure 26).<br />
The proteins were classified into three groups depending on the ASH and the corresponding<br />
salt concentrations on which the proteins were eluted. The proteins eluted at high salt<br />
concentrations in the beginning of the chromatography have less ASH values and were<br />
considered as less hydrophobic than the proteins eluted at the middle and / or at the end of the<br />
chromatography. Those proteins which have many hydrophobic residues on their surfaces<br />
were retained longer and eluted at the end of the chromatography. The data obtained for the<br />
proteins eluted at low salt concentrations such as 0.6 M, 0.2 M and 0 M have high importance<br />
for industrial applications, where the use of low salts is the preferred choice in HIC (24). ASH<br />
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Chapter 3<br />
values have been reported on average 0.403, 0.449 and 0.519 for the proteins eluted at the<br />
beginning, middle and at the end of the chromatography, respectively. These hydrophobicity<br />
values were in agreement to those in the literature (32, 62). This revealed that proteins with<br />
less ASH would retain for a shorter space of time than the protein of high surface<br />
hydrophobicity. The surface hydrophobicity can also be related to the hydrophobic surface<br />
area of a protein and both have linear relations between them and the retention volume (127).<br />
Figure 26: Represents the average surface hydrophobicity calculated by Cowan-Whittaker’s<br />
scale. The protein average surface hydrophobicity values of the three base supports were<br />
correlated with the respective salt concentrations where the fractions were collected. The error<br />
bars represent the standard deviations of the average surface hydrophobicity values in each<br />
fraction. The statistical analysis on average revealed a significant correlation (r 2 = 0.91; p <<br />
0.002).<br />
94
Chapter 3<br />
The correlation coefficients (r 2 ) of ASH with retention of proteins were 0.93, 0.83 and 0.96<br />
for Toyopearl-Phenyl, Sepharose-Phenyl and Source-Phenyl, respectively. The correlation<br />
coefficient reported in the Sepharose-Phenyl case was less than Toyopearl-Phenyl and<br />
Source-Phenyl; the reason could be the heterogeneous distribution of the hydrophobic<br />
hotspots on the base support. This kind of behavior was previously reported with<br />
chromatographic separation of model proteins by Sepharose-Phenyl, where proteins of similar<br />
ASH have exhibited different retention times (63).<br />
In the previous reports, model proteins have been used to correlate ASH with the retention<br />
behavior. This correlation was never examined as a function of the complex cell proteome (26,<br />
27). The correlation of ASH with retention behavior was explored for the first time in this<br />
work with the yeast cell proteome. The experimental data about salt concentrations and<br />
corresponding hydrophobicity ranges at different elution stages can be exploited to predict the<br />
separation of recombinant proteins employing yeast as a host cell proteome. The separation of<br />
recombinant proteins by HIC can also be predicted in other host cell proteomes, once its ASH<br />
is calculated. ASH was the only property which has revealed the trend lines to partially<br />
differentiate among the base supports.<br />
3.2.3.3.4. Other protein properties affecting the chromatographic behavior<br />
The average flexibility was calculated for the tabulated proteins using the scale proposed by<br />
Bhaskaran and Ponnuswamy (Tables 12-14) (98). The average flexibility has revealed a<br />
statistically significant (r 2 = 0.80; p < 0.0001) direct correlation with the protein retention<br />
during chromatography (Figure 27). The average flexibility has been reported before for its<br />
direct relationship with the retention time of model proteins in HIC (62). This could be due to<br />
the presence of cystein residues in a protein forming disulfide bridges. A protein with more<br />
cystein content / and or disulfide bridges will be highly stable and less flexible towards<br />
conformational changes. In contrast, proteins with less cystein residues will be highly flexible<br />
95
Chapter 3<br />
towards conformational changes and when it comes in contact of HIC surface, it will unfold<br />
or expose the internal surface in order to increase hydrophobic interactions and result in<br />
longer retention during chromatography (33, 62, 128).<br />
Figure 27: Represents the average flexibility calculated by Bhaskaran and Ponnuswamy’s<br />
scale. The protein average flexibility values of the three base supports were correlated with the<br />
respective salt concentrations where the fractions were collected. The error bars represent the<br />
standard deviations of the average flexibility values in each fraction. The statistical analysis<br />
revealed a significant correlation (r 2 = 0.80; p < 0.0001).<br />
It was also reported by Tanford that proteins with native conformations were completely<br />
inflexible and conformational entropy was zero. In contrast, the unfolded proteins have high<br />
flexibility towards conformational changes (25). This revealed that proteins with less<br />
flexibility had less conformational entropy and high stability. In this work, average flexibility<br />
was correlated with the retention of proteins derived from a host cell proteome. Some proteins<br />
96
Chapter 3<br />
such as D3-SP, B4-PS and B4-SP were eluted earlier during chromatography than E1-SP, C1-<br />
PS and C1-SP, respectively although they have almost similar ASH. This could be due to the<br />
higher flexibilities of the proteins which allow them to retain longer during chromatography.<br />
The same behavior was also reported with model proteins where ribonuclease S has shown<br />
higher retention on Sepharose-Butyl than other proteins, although they had almost identical<br />
surface hydrophobicity. It was claimed that higher flexibility of ribonulcease S might be<br />
favoring longer retention (63). The highly flexible proteins have revealed more unfolding and<br />
most of the hydrophobic residues were exposed which has given tight binding and in result<br />
most of them eluted at the end of the chromatography. Less flexible proteins were not able to<br />
stay longer due to their compact nature and most of hydrophobic parts were in the inner core,<br />
which resulted in less hydrophobic interactions and elution occurred at the beginning of the<br />
chromatography. Although the flexibility has no decisive role in retention, however it can be<br />
considered as the contributing parameter during HIC. The flexibility has given a correlation<br />
with retention of proteins; however it has not exhibited any differences among the base<br />
supports for their hydrophobic characters.<br />
The average bulkiness was also calculated for the tabulated proteins using Zimmerman’s scale<br />
(Tables 12-14). The average bulkiness of the proteins has revealed no significant relationship<br />
(r 2 = 0.04, p = 0.38) with the chromatographic separation of proteins in HIC (Figure 28). The<br />
bulkiness has been never reported for its contribution in HIC. In the previous report,<br />
Zimmerman had investigated to correlate polarity and bulkiness of cytochrome c protein in<br />
different organisms (96). However, no significant trend was observed between polarity and<br />
bulkiness in his studies. This also confirmed that if bulkiness has no relation with the polarity<br />
of the proteins, then it is supposed to have no relation with protein hydrophobicity as<br />
evidenced here. The bulkiness has also exhibited no differences among the different base<br />
supports.<br />
97
Chapter 3<br />
Figure 28: Represents the average bulkiness calculated by Zimmerman’s scale. The protein<br />
average bulkiness values of the three base supports were correlated with the respective salt<br />
concentrations where the fractions were collected. The error bars represent the standard<br />
deviations of the average bulkiness values in each fraction. The statistical analysis revealed a<br />
non significant correlation (r 2 = 0.04; p = 0.38).<br />
3.2.3.4. Comparative studies of the different base supports based on average protein<br />
properties<br />
The comparison of the base supports has been done for the first time in this work with the<br />
proteome wide approach. The protein properties such as average surface hydrophobicity,<br />
average hydrophobicity, average flexibility and average polarity have shown a correlation<br />
with the retention of proteins during chromatography. However, these properties have a<br />
limited ability to differentiate among the base supports for their hydrophobicity (Table 15).<br />
98
Chapter 3<br />
Table 15: Average protein properties for their correlation with retention time and the ability to<br />
differentiate among the base support for their hydrophobicity.<br />
Protein properties<br />
Differentiation<br />
Correlation<br />
Average hydrophobicity × +<br />
Average polarity ×<br />
_<br />
Average flexibility × +<br />
Average bulkiness × ×<br />
Average surface hydrophobicity Trend +<br />
+ indicates positive correlation, - indicates negative correlation; × indicates no correlation<br />
These results revealed that protein properties have a limited ability to differentiate among the<br />
base supports for their hydrophobicity. At any specific fraction of time, several proteins are<br />
supposed to be eluted during chromatographic separation of a cell proteome. Due to this<br />
reason, assigning a specific retention time to any protein is impractical. This revealed that the<br />
proteome wide approach has a limited ability to differentiate among the base supports.<br />
In two cases (1.6 M and 0 M), the Sepharose-Phenyl has given slightly high hydrophobic<br />
proteins than Toyopearl-Phenyl. This unexpected behavior of Sepharose-Phenyl was also<br />
reported before; where proteins with high ASH were eluted earlier than those with lower ASH.<br />
The reason could be the heterogeneous distribution of hydrophobic hotspots on the<br />
Sepharose-Phenyl, due to which an unusual behavior was reported in the retention mechanism<br />
(63). Another observation was that base supports revealed less differences at high salt<br />
concentrations than at low salt concentrations. In other words, the differences among the base<br />
supports were less at high hydrophobic conditions than at low hydrophobic conditions. This<br />
kind of behavior was also reported in our group with model proteins, where the differences<br />
99
Chapter 3<br />
among the adsorbents were higher at low salt concentrations than at the high salt<br />
concentrations (121). The reason for this could be that at the extreme hydrophobic conditions,<br />
the hydrophobic potential of the mobile phase has covered the potential of the base supports<br />
to demonstrate according to their hydrophobicities. At low salt concentrations, the mobile<br />
phase has low hydrophobicity and the adsorption was more dependent on the hydrophobicity<br />
of the base supports.<br />
3.2.3.5. Application note<br />
This work can help those interested in the purification of proteins listed in Tables 12-14. It<br />
will simplify the work of researchers and they will not have to scout for chromatographic<br />
experiments to figure out the elution position of any tabulated protein. The protein can be<br />
purified by isocratic chromatography, without going through trial and error chromatographies.<br />
Some of the important enzymes are Pyruvate kinase (eluted at 0.6 M on Sepharose-Phenyl),<br />
Phosphoglycerate mutase 2 (eluted at 0.8 M on Toyopearl-Phenyl), Polymerase A (0 M on<br />
Source-Pheny), Methyl transferase (0.2 M on Sepharose-Phenyl), Thiosulfate sulphur<br />
transferase (0.2 M on Source-Phenyl), Enolase 2 (1.6 M on Toyopearl-Phenyl), D-serine<br />
dehydratase (0.6 M on Toyopearl-Phenyl) and Alcohol dehydrogenase (0.2 M on Sepharose-<br />
Phenyl).<br />
100
Chapter 3<br />
3.2.3. Partial conclusions<br />
• The influence of the base supports has been investigated for the first time with the<br />
proteome wide approach utilizing different protein properties. The results revealed<br />
that protein properties have limited ability to differentiate among the base supports.<br />
• The base supports revealed less difference at high salt concentrations than at low salt<br />
concentrations. In other words, the differences among the base supports were less at<br />
high hydrophobic conditions than at low hydrophobic conditions. The reason could be<br />
the effects of mobile phase hydrophobicity.<br />
• Several protein properties such as average hydrophobicity, average flexibility and<br />
average surface hydrophobicity have revealed direct relationships with the retention of<br />
proteins. Average polarity has given an inverse relationship to the retention of proteins<br />
during HIC.<br />
• Among all the properties, ASH was the only property which has a decisive role in the<br />
retention behavior of proteins during chromatography.<br />
• The data collected from the three base supports in this process proteomics approach<br />
can be used to design the purification models by the in silico approach.<br />
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Chapter 3<br />
3.3. Protein distributions on 2-D gels: the influence of molecular<br />
weight and isoelectric point coordinates<br />
3.3.1. Summary<br />
The separation behavior of the yeast cell proteome by hydrophobic interaction<br />
chromatography (HIC) was examined utilizing two dimensional polyacrylamide gel<br />
electrophoresis (2-D PAGE). The yeast cell proteome was fractionated on different<br />
hydrophobic adsorbents and chromatographic fractions were collected on specific salt<br />
concentrations. The chromatographic fractions were further analyzed by 2-D PAGE to<br />
investigate the contaminant profile in each chromatographic fraction. This chapter will give<br />
an additional understanding to the influence of ligands and base supports on the<br />
chromatographic separation of the yeast cell proteome. As expected, Toyopearl-Hexyl and<br />
Toyopearl-Butyl have shown increased protein retention in comparison with Toyopearl-Ether.<br />
Moreover, an influence of protein size and isoelectric point (pI) was also revealed as a<br />
function of the adsorbent type. Larger and / or neutral proteins showed increased while acidic<br />
(pI < 6) and basic (pI > 8) proteins depicted decreased or increased retention, depending on<br />
the adsorbent type.<br />
In addition to the separation behavior of yeast cell proteome during chromatography, an<br />
overview of the yeast cell proteome was gained. A high amount and a higher number of the<br />
proteins on 2-D gels were reported at the beginning of the chromatography than at the end.<br />
This confirmed the less hydrophobic nature of the yeast cell proteome. A higher number of<br />
smaller and basic proteins were observed on 2-D gels in comparison to larger and acidic<br />
proteins. This further showed the nature of the yeast cell proteome.<br />
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Chapter 3<br />
3.3.2. Results and Discussion<br />
3.3.2.1. Unfolding of proteins during HIC<br />
The 2-D gels were performed for all the chromatographic fractions collected through six<br />
different hydrophobic adsorbents and stained with colloidal blue method in order to get the<br />
precise number of spots available in each fraction. The 2-D gels have been presented in<br />
Figures 12-14 (Section 3.1) and Figures 21-23 (Section 3.2). Some denatured spots were<br />
observed for the fractions collected at high salt concentrations on Source-Phenyl and<br />
Toyopearl-Hexyl representing the conformational changes occurred during chromatography.<br />
However, Toyopearl-Butyl and Sepharose-Phenyl has revealed less conformational changes<br />
and solid spots were observed on the 2-D gels. The conformational changes at high salt<br />
concentrations during chromatography were also reported before with model proteins (122,<br />
129-131). The ratio of the conformational changes of proteins was high on the gels collected<br />
at the high salt concentrations and extreme hydrophobic adsorbents. This revealed that<br />
conformational changes during chromatography have dependence on the salt concentration<br />
and the adsorbent type. This behavior was also reported before that by increasing the<br />
ammonium sulfate concentration and the chain length of ligands, the unfolding of protein<br />
increases on the HIC surface. This is the reason that unnecessary high salt concentration and<br />
high chain lengths of the ligands should not be preferred (129). The conformational changes<br />
observed on 2-D gels further reinforced the correlations observed in Section 3.1 and Section<br />
3.2, between flexibility and the corresponding retention volume. Some protein loss was also<br />
observed while comparing the amount of sample applied during chromatography and proteins<br />
obtained in the fractions <strong>after</strong> chromatography. Small amount of proteins (almost 5 mg) was<br />
lost elsewhere on the hydrophobic surface. The reason could be the unfolding of the proteins<br />
which resulted in less recovery in the chromatography fractions. These results confirmed the<br />
protein unfolding during HIC with the proteome wide approach.<br />
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Chapter 3<br />
3.3.2.2. Protein distributions on 2-D gels<br />
Proteins derived from S. cerevisiae cell proteome have shown less binding affinity on the<br />
adsorbents based on hydrophobic interactions. A maximum number and a high amount (mg)<br />
of proteins were eluted at the beginning of the chromatography than at the end (Figure 29 A-<br />
B). These results confirmed the less hydrophobic nature of S. cerevisiae cell proteome. The<br />
hydrophobic proteins in the S. cerevisiae cell proteome were reported to be 16% of the total<br />
cell proteome. This was very less amount than reported for other cell proteomes (104). The<br />
less hydrophobic nature of the yeast cell proteome suggested high hydrophobic conditions for<br />
the efficient separation of the cell proteome during HIC. The analysis of 2-D gels has also<br />
revealed the variation in the binding affinity of the different ligands for the same proteome. In<br />
case of Toyopearl-Ether, a higher number of protein spots were eluted at the beginning of the<br />
chromatography than at the end. The reason could be the less hydrophobic nature of the<br />
Toyopearl-Ether. In contrast, Toyopearl-Hexyl has shown higher numbers of protein spots at<br />
the end of the chromatography than at the beginning because of its high hydrophobic nature.<br />
In the similar fashion, the 2-D gels analysis has also revealed the variation in the binding<br />
affinity of the base supports for the same proteome. In case of Toyopearl-Phenyl, a higher<br />
number of proteins were eluted in the beginning of the chromatography than at the end and<br />
the reason could be the less hydrophobic nature of the base support. Sepharose-Phenyl and<br />
Source-Phenyl have shown higher numbers of spots at the end of the chromatography. This<br />
revealed the high binding affinity of the two adsorbents for the yeast cell proteome.<br />
When the adsorption affinity of polymethacrylate based media was compared with phenyl<br />
ligated media, the adsorption affinity was higher in earlier than later one. Toyopearl-Phenyl<br />
with phenyl ligand has not given higher adsorption of proteins when compared to Toyopearl-<br />
Butyl and Toyopearl-Hexyl. This confirmed that aromatic interactions have no effect on the<br />
adsorption affinity of the adsorbents (122, 132).<br />
104
Chapter 3<br />
Figure 29 A: Represent the total number of spots and amount of proteins (mg) in each fraction<br />
as a function of the different base supports. The number of protein spots and amount of<br />
proteins (mg) revealed a significant correlation with the respective salt concentrations where<br />
the fractions were collected.<br />
105
Chapter 3<br />
Figure 29 B: Represent the total number of spots and amount of proteins (mg) in each fraction<br />
as a function of the different ligands. The number of protein spots and amount of proteins (mg)<br />
revealed a significant correlation with the respective salt concentrations where the fractions<br />
were collected.<br />
3.3.2.3. The effect of protein molecular mass on the chromatographic behavior<br />
Other component in evaluating the experimental data obtained <strong>after</strong> 2-D gel electrophoresis is<br />
the potential relationship between protein molecular weight and elution behavior, as a<br />
function of elution position and adsorbent type. In a previous report, molecular mass of the<br />
protein has not shown any correlation with protein retention in ion exchange chromatography<br />
(19). However, the effect of molecular mass was reported in HIC using the retention time of<br />
the model proteins (33). A few model proteins were used for the analysis which may not give<br />
a complete picture of all the contributing parameters involved in protein retention. In this<br />
106
Chapter 3<br />
work, the effects of larger proteins (> 50 KDa) on retention have been examined as a function<br />
of the adsorbent type. The number of larger proteins in each fraction was divided by the total<br />
number of proteins to normalize the data. The normalized values of the larger proteins from<br />
the three ligands was presented in Figure 30, where the larger proteins were found to be<br />
higher at low salt concentrations than at high salt concentrations. In other words, the larger<br />
proteins were higher in the latter fractions in comparison to the earlier ones. This revealed that<br />
larger proteins retained longer on the adsorbents. In case of larger proteins, the retention was<br />
higher for Toyopearl-Butyl and Toyopearl-Hexyl in comparison to the other adsorbents. It<br />
was reported before that if the pore space of the adsorbent is smaller than the dimensions of<br />
the magnitude as the protein has, then it will result in less retention of protein (33). However,<br />
this was not applicable on Toyopearl-Ether and Toyopearl-Phenyl adsorbents where larger<br />
proteins has shown less retention, although they have high pore size (100 nm). The reason<br />
could be the less hydrophobic nature of the two adsorbents which retained larger proteins for<br />
a shorter time during chromatography. In a similar way, Source-Phenyl has given less<br />
retention to larger proteins, although it has a high hydrophobic nature, but the pore size is<br />
smaller (34 nm). These results declared that the pore size and hydrophobicity of the<br />
adsorbents could be the combine contributing parameters affecting the retention of larger<br />
proteins.<br />
In case of smaller proteins it was reported once that it has no effect on retention (33).<br />
However, in another report it was reported that smaller proteins exhibited a decrease in<br />
retention due to the less contact surface area with adsorbents (68). This was confirmed in this<br />
work when smaller proteins have exhibited an inverse relationship to the corresponding<br />
retention behavior and most of the smaller proteins were eluted at the beginning of the<br />
chromatography. The reason could be that smaller proteins have less non-polar surface area to<br />
interact with the adsorbents.<br />
107
Chapter 3<br />
When the number of smaller and larger proteins was compared, smaller proteins were three<br />
times higher than larger proteins on the 2-D gels. This revealed that S. cerevisiae cell<br />
proteome have a higher proportion of smaller proteins than larger proteins.<br />
Figure 30: Represents the normalized values of the larger proteins (Mr > 50) in each fraction as<br />
a function of the different ligands. The larger proteins revealed a significant correlation with<br />
the respective salt concentrations where the fractions were collected.<br />
3.3.2.3. The influence of protein isoelectric point on the chromatographic behavior<br />
The effect of mobile phase pH and isoelectric point of the proteins are the parameters least<br />
studied in HIC. At the start of HIC method, it was reported that there is no obvious<br />
relationship between retention and isoelectric point of individual proteins (68). However, in<br />
the follow up studies some clear pH effects were observed, where basic proteins has shown<br />
longer retention when the mobile phase pH was close to their pI and acidic proteins exhibited<br />
less retention with basic pH values. In other words, the protein retention was increased at<br />
108
Chapter 3<br />
acidic pH and decreased at basic pH values of the mobile phase (43). To study the sole effect<br />
of the pI, a neutral pH was maintained during chromatographic experiments. The neutral pH<br />
is also a preferred choice in the downstream processing of proteins. The chromatographic<br />
fractions were analyzed on 2-D gels and the normalized values of the acidic and basic proteins<br />
were calculated. The normalized values of the basic proteins in each gel (fraction) were<br />
presented versus corresponding elution position for different ligands (Figure 31). A higher<br />
number of basic proteins were found at low salt concentrations at the end of the<br />
chromatography which revealed the longer retention of basic proteins during chromatography.<br />
The Sepharose-Phenyl and Toyopearl-Butyl have given longer retention to the basic proteins<br />
in comparison to the other adsorbents. This revealed the partial negative charges on these<br />
adsorbents. The negative charges on adsorbents may attract the positive charges on basic<br />
proteins and resulted in ionic interactions in addition to HIC. The combination of the ionic<br />
and hydrophobic interactions resulted in longer retention of the basic proteins during<br />
chromatography. The partial negative charges on hydrophobic adsorbents were also reported<br />
in literature, although the ration of negative charges varied among adsorbents (133). The<br />
uncharged adsorbents were reported to be preferred for the true hydrophobic interactions,<br />
otherwise at low salt concentrations; the interaction will be more electrostatic than<br />
hydrophobic, which can affect the separation based on hydrophobic interactions (43).<br />
However, in other reports the presence of charges on the adsorbents was favored in order to<br />
maintain the native structures of the proteins (21, 102).<br />
The neutral proteins pI (6-8) and acidic proteins (pI < 6) have been eluted in almost equal<br />
distribution during the chromatographic separations. The reason could be the neutral pH of<br />
the mobile phase, which allowed the neutral proteins to elute on the basis of hydrophobicity.<br />
The analysis of 2-D gels revealed that the gels at the end have mostly neutral proteins, while<br />
acidic and basic proteins are rarely available. The proteins with pI around 6, 7 and 8 have<br />
been highly retained at neutral pH and the reason could be that the charge was near to zero<br />
109
Chapter 3<br />
and hydrophobic interactions were at the maximum extent between proteins and adsorbents<br />
(44). These results confirmed the effects of isoelectric point values of the proteins on the<br />
chromatographic separation as a function of adsorbent type. A high number of basic proteins<br />
have been reported on 2-D gels in comparison to acidic proteins. This revealed the high<br />
abundance of basic proteins in S. cerevisiae cell proteome.<br />
Figure 31: Represents the normalized values of the basic proteins (pI > 8) in each fraction as a<br />
function of different ligands. The basic proteins revealed a significant correlation with the<br />
respective salt concentrations where the fractions were collected.<br />
110
Chapter 3<br />
3.3.3. Partial conclusions<br />
• A high amount and a higher number of protein spots were reported at the beginning of<br />
the chromatography than at the end. This confirmed the less hydrophobic nature of the<br />
S. cerevisiae cell proteome.<br />
• The denaturation of proteins was confirmed in HIC with the proteome wide approach.<br />
• Aromatic interactions were confirmed to have no effect on the retention behavior of<br />
proteins during HIC.<br />
• The effect of molecular weight of the proteins was studied in relation to the adsorbent<br />
type. The pore size and hydrophobicity of the adsorbents were reported as the main<br />
parameters responsible for the retention of the larger proteins.<br />
• The effect of the isoelectric point of proteins was studied in relation to adsorbent type.<br />
The neutral and acidic proteins have given equal distributions over 2-D gels. The<br />
partial negative charges were confirmed on the hydrophobic adsorbents which has<br />
given rise to ionic interactions in addition to hydrophobic interactions and resulted in<br />
longer retention of basic proteins during chromatography.<br />
• A higher number of acidic and smaller proteins were reported in the cell proteome in<br />
comparison to basic and larger proteins, which can further reflect the nature of S.<br />
cerevisiae cell proteome.<br />
111
Chapter 4<br />
CONCLUSIONS
Chapter 4<br />
4. Conclusions and Remarks<br />
4.1. General conclusions and remarks<br />
This thesis explored for the first time the comparative analysis of hydrophobic ligands and<br />
base supports in hydrophobic interaction chromatography (HIC) as a function of complex cell<br />
proteome. Several protein properties were examined for their correlation with the protein<br />
retention behavior in HIC. The protein properties were also explored to determine their<br />
potential to differentiate among the ligands and base supports. The previous studies of HIC<br />
were based on the retention time of model proteins. Hence, a process proteomics approach<br />
was applied to explore the hidden underlying parameters in HIC. Although very laborious and<br />
experimentally demanding, the present approach has led to conclusions which are valid for a<br />
complex cell proteome and which refers to two families of hydrophobic adsorbents. This<br />
work has produced a first draft of the experimental data which can be used to predict the<br />
elution position of any target protein utilizing an in silico approach.<br />
4.2. Influence of different ligands<br />
• The hydrophobicity of the ligands was comparatively investigated in the previous<br />
reports exploiting the retention time of model proteins. However, the hydrophobicity<br />
of the ligands has been investigated for the first time with the yeast cell proteome<br />
utilizing several protein properties. The protein properties have not fully differentiated<br />
among the ligands. This confirmed that protein properties have limited ability to<br />
differentiate among the ligands for their hydrophobicity.<br />
4.3. Influence of different base supports<br />
• The influence of the base supports has been investigated for the first time with the<br />
proteome wide approach utilizing different protein properties. The results revealed that<br />
protein properties have limited ability to differentiate among the base supports.<br />
113
Chapter 4<br />
• The base supports revealed less difference at high salt concentrations than at low salt<br />
concentrations. In other words, the differences among the base supports were less at<br />
high hydrophobic conditions than at low hydrophobic conditions. The reason could be<br />
the effects of mobile phase hydrophobic conditions.<br />
4.4. Effect of protein properties on separation behavior during chromatography<br />
• Several protein properties were investigated for the first time with the proteome wide<br />
approach for their effects on the protein retention behavior during HIC.<br />
• The average surface hydrophobicity (ASH), average hydrophobicity (AH) and average<br />
flexibility (AF) have revealed direct relationships with the retention behavior of the<br />
proteins in HIC.<br />
• The average bulkiness of the proteins did not show any relationship with the retention<br />
behavior during chromatography.<br />
• The polarity of the proteins exhibited an inverse relationship with the retention of the<br />
proteins. The higher the polarity of the protein, the less time it will stay on the<br />
adsorbent during chromatography and vice versa.<br />
• An indirect relationship was observed between polarity and hydrophobicity; proteins<br />
with high polarity were less hydrophobic in nature and vice versa.<br />
• An indirect relationship was also observed between polarity and flexibility; proteins<br />
with high polarity were less flexible in nature and vice versa.<br />
• Among all the properties, ASH was the only property which has a decisive role in the<br />
retention behavior of the proteins during HIC.<br />
• The salt and ASH ranges were defined for the first time during the chromatographic<br />
fractionation of the yeast cell proteome. The information gathered about ASH and the<br />
retention position during chromatography can be exploited for the chromatographic<br />
separation of recombinant proteins from host cell proteomes.<br />
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Chapter 4<br />
• The experimental data obtained with this proteome wide approach can be used to<br />
design the purification models utilizing the in silico approach.<br />
4.5. 2-D gels analysis<br />
• A high amount and a higher number of protein spots were reported at the beginning of<br />
the chromatography than at the end. This result confirmed the less hydrophobic nature<br />
of the S. cerevisiae cell proteome.<br />
• A higher number of smaller and basic proteins were reported than larger and acidic<br />
proteins which can further reflect the nature of the S. cerevisiae cell proteome.<br />
• The denaturation of proteins in HIC was confirmed with the proteome wide approach.<br />
• A large number of proteins were retained on Toyopearl-Butyl than Toyopearl-Phenyl,<br />
this confirmed that aromatic interactions have no effect on the retention behavior<br />
during HIC.<br />
• The effect of molecular weight of the proteins was studied in relation to the adsorbent<br />
type. The pore size and hydrophobicity of the adsorbents were concluded to be the<br />
main parameters responsible for the retention of larger proteins in HIC.<br />
• The effect of isoelectric point of the proteins was studied in relation to the adsorbent<br />
type. The neutral and acidic proteins were equally distributed over the 2-D gels. Basic<br />
proteins retained longer due to the presence of partial negative charges on the<br />
adsorbents which has given rise to ionic interactions in addition to hydrophobic<br />
interactions and resulted in longer retention of basic proteins.<br />
4.5. Additional conclusions<br />
• In total, 173 proteins were identified and the in-gel digestion protocol was optimized<br />
specifically for the proteins fractionated by HIC. The identified proteins can provide<br />
115
Chapter 4<br />
additional understanding of the protein contaminants present in the yeast cell proteome<br />
and is a valuable addition to the proteomics technology.<br />
• Several important enzymes have been identified which were eluted at specific salt<br />
concentrations. These enzymes can be purified by a single isocratic chromatography<br />
without going through trial and error chromatographies to figure out the elution<br />
position of any enzyme.<br />
4.6. Recommendations for future work<br />
• The average protein properties derived from the primary structure need to be explored<br />
in reverse phase chromatography with the process proteomics approach.<br />
• The average polarity parameter can be calculated from the surface residues of the three<br />
dimensional structure of a protein. This average surface polarity can be used as an<br />
alternative parameter to ASH.<br />
• The adsorbent such as Toyopearl-Ether revealed an interesting chromatographic<br />
behavior and can be used in future for the purification of the recombinant proteins of<br />
highly hydrophobic nature.<br />
• This approach can be extended to other hydrophobic adsorbents and expression<br />
systems such as E.coli, mammalian cells and insect cells for the development of<br />
downstream processing.<br />
• This work involved the analysis of a real expression host instead of using model<br />
proteins and the data obtained can be used to design a purification model for the<br />
recombinant proteins utilizing an in silico approach.<br />
116
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