Thesis final - after defense-7 - Jacobs University

A proteome wide approach to understand 

chromatography behavior on hydrophobic 

adsorbents 

by 

Nawab Ali 

A Thesis submitted in partial fulfillment 

of the requirements for the degree of 

Doctor of Philosophy 

in Biochemical Engineering 

Approved Dissertation Committee 

__________________________________ 

Prof. Dr. Marcelo Fernández-Lahore 

Prof. Dr. Matthias Ullrich 

Dr. Abid Riaz Ahmad 

Date of Defense: 

June 05 th , 2012 

School of Engineering and Science

Acknowledgement 

All praise for Almighty ALLAH, Without Allah’s help, I would not be able to achieve 

anything in my life. 

I am greatly honored to owe my deepest thanks to my thesis supervisor Prof. Dr. Marcelo 

Fernández-Lahore for giving me the opportunity to carry out my PhD work under his kind 

supervision and accepting me in his international group. I greatly appreciate his valuable 

suggestions and supervision in my PhD work. I am highly thankful to him for accepting my 

wife as a graduate student under his kind supervision. He has given me an opportunity to 

perform my tasks independently which increased my curiosity for research and which 

accomplishes the true spirits of PhD. I am thankful for his student friendly and cooperative 

attitude in the last four years. 

I am also thankful to Prof. Dr. Matthias Ullrich and Dr. Abid Riaz Ahmad for their comments 

and nice suggestions for the improvement of the thesis. 

I also want to thank and acknowledge Muhammed Mobashir and Abdul Wakeel for helping 

me in the bioinformatics part of this work. 

I convey my heartiest acknowledgements to my group fellows Aasim, Zia, Nadia, Noor Shad, 

Farhat, Amir, Tuhidul Islam, Sirma, Amira, Rajesh, Dr Rami, Dr. Sonja, Miss. Nina, Antonio, 

Doreen, Iza, Naveen, Prasad, Rodrigo and Zumrad. 

Words are lacking to present my feeling for the prayers and struggles done by my parents 

throughout my life from childhood until now. I am now in this position due to my father 

continuous guidance and efforts during my education. I am also thankful to my brother 

Naveed Ali, sisters and brother in law; Sadeeq and Sabir for their love and care at each step of 

my life. I also want to acknowledge my other relatives and uncles Mumtaz Ali, Ihsan Ali, 

Qamer Zaman and my cousins Nazrali, Mohsin, Jenab, Aasim, Zafar, Amin, Khaild. 

I

I also want to mention the efforts of my wife Nadia for her patience and support during my 

PhD studies when I was in tough time of having funding and other research problems. I am 

also thankful to her for letting me free from other homely responsibilities when I was busy in 

my research work. 

I also want to convey my heartiest and sincerest acknowledgements to my closest friends 

Ammar, Qasim, Aasim, Yaqoob, Noor Muhammad saib, Abunaser saib, Farhan, Hamish saib, 

Farrukh saib, Roohalamin saib, Noor Shad, Zia, Wakeel, Sadiq, Amna, Farhat, Qazi, Hazir, 

Tariq, Inam, Tahir, Ali, Imran, Rehan, Saleem, Shahzad. 

Finally, I wish to thank Kohat University of Science and Technology (KUST), Khyber 

Pukhtunkhwa, Pakistan and Higher Education Commission, Islamabad, Pakistan for providing 

me opportunity for higher studies abroad. 

II

Nomenclature 

S. cerevisiae Saccharomyces cerevisiae 

HIC 

Hydrophobic interaction chromatography 

ASH 

Average surface hydrophobicity 

PDB 

Protein databank 

AH 

Average hydrophobicity 

ASA 

Accessible surface area 

CW 

Cowan-Whittaker scale 

MJ 

Miyazawa-Jernigan scale 

T 

Tanford scale 

AF 

Average flexibility 

CV 

Column volume 

mAU 

Milli absorbance unit 

IEF 

Isoelectric focussing 

TEMED 

N, N, N, N-tetramethyl-ethylenediamine 

DTT 

Dithiothreitol 

Mili Q water water purified with millipore system 

SDS-PAGE Sodium dodecyl polyacrylamide gel electrophoresis 

MALDI-ToF-MS Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass spectrometry 

2-D PAGE Two dimensional polyacrylamide gel electrophoresis 

PS 

Phenyl-Sepharose 

SP 

Source-Phenyl 

TP 

Toyopearl-Phenyl 

TB 

Toyopearl-Butyl 

TH 

Toyopearl-Hexyl 

TE 

Toyopearl-Ether 

S j 

S i 

Sum of total surface area of the proteins 

Sum of accessible surface area of for all the amino acids of type i 

r i Superficial area of amino acid for all the amino acids of class i. 

φ 

i 

Hydrophobicity of amino acids for all the amino acids of class i 

Φ surface Average surface hydrophobicity 

Mr 

pI 

Molecular weight of the protein 

Isoelectric point of the protein 

III

Dedication 

To my father Haji Sher Afzal and mother Kamkhwaba for their continuous support and 

efforts during my education and in every aspect of my life. 

IV

Abstract 

The separation behavior of the Saccharomyces cerevisiae cell proteome was explored by 

hydrophobic interaction chromatography (HIC). Several beaded adsorbents were studied. A 

first group of HIC adsorbents (n = 3) harbored the same ligand moiety (i.e., Phenyl) but 

presented differences in the chemical nature of the matrix backbone. On the other hand, a 

second group of adsorbents (n = 3) presented the same chemical structure (i.e. based on 

polymethacrylate; Toyopearl / TP) but differed in the chemical nature of the HIC ligands. 

Chromatography runs were performed under typical conditions employing ammonium sulfate 

/ phosphate buffer (pH = 7.5) as a mobile phase. Chromatography fractions were collected 

and further analyzed by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE); 

selected spots were identified by MALDI-ToF-MS and database search. 

A comparative analysis of protein separation behavior is presented as a function of the 

adsorbent type. As expected, TP-Hexyl and TP-Butyl showed increased protein retention in 

comparison with TP-Ether. Moreover, an influence of protein size and isoelectric point (pI) 

was also revealed. Larger and / or neutral proteins showed increased while acidic (pI < 6) and 

basic (pI > 8) proteins depicted decreased or increased retention, depending on the adsorbenttype. 

Protein characteristics such as average surface hydrophobicity, average hydrophobicity 

average polarity, average bulkiness and average flexibility were tabulated for the identified 

spots and correlated with chromatography behavior. The proteins properties revealed limited 

ability to differentiate among the ligands and base supports for their hydrophobicity. This 

work -for the first time- studied the separation behavior of a real and complex cell proteome 

as a function of commercially available HIC adsorbents. The mentioned results will favor the 

understanding and the application of HIC, a method of choice in many industrial bioseparation 

schemes. 

I

Table of Contents 

1. Introduction ............................................................................................................................ 1 

1.1. Introduction to Biotechnology ........................................................................................ 1 

1.2. Downstream processing .................................................................................................. 2 

1.3. Hydrophobic interaction chromatography (HIC) ............................................................ 4 

1.4. Factors affecting protein chromatography behavior in HIC ........................................... 7 

1.4.1. Effect of mobile phase parameters ........................................................................... 7 

1.4.2. Effect of stationary phases in HIC ......................................................................... 10 

1.4.3. Effect of protein properties in HIC ........................................................................ 12 

1.4.4. The comparison between HIC and RPC ................................................................ 13 

1.5. Analysis of the yeast proteome as biotech feedstock .................................................... 15 

1.6. Mass spectrometry and database searching ................................................................... 16 

1.7. The in silico Approach .................................................................................................. 18 

1.8. Goal of the work ............................................................................................................ 21 

2. Experimental approach ......................................................................................................... 24 

2.1. Materials ........................................................................................................................ 24 

2.2. Experimental strategy .................................................................................................... 24 

2.2.1. Cell Cultivation ...................................................................................................... 26 

2.2.2. Preparation of the crude extract ............................................................................. 26 

2.2.3. Chromatographic fractionation .............................................................................. 26 

2.2.4. Membrane dialysis ................................................................................................. 27 

2.2.5. Two dimensional polyacrylamide gel electrophoresis (2-D PAGE) ...................... 27 

2.2.6. Protein content of the fractions .............................................................................. 29 

2.2.7. Protein identification by MALDI-ToF-MS ............................................................ 29 

2.2.8. Characterization of proteins utilizing Bioinformics ............................................... 31 

3. Results and Discussion ......................................................................................................... 35 

3.1. Influence of the different ligand chemistries during hydrophobic interaction 

chromatography ........................................................................................................................ 35 

3.1.1. Summary .................................................................................................................... 35 

3.1.2. Scientific background of the approach ....................................................................... 36 

3.1.2.1. Hydrophobic adsorbents ...................................................................................... 36 

3.1.2.2. Protein properties in the context of HIC ............................................................. 39 

3.1.2.3. Towards an in silico approach for the downstream processing of bioproducts .. 43 

2

3.1.3. Results and Discussion ............................................................................................... 45 

3.1.3.1. Chromatographic profiles of different ligands .................................................... 45 

3.1.3.2. Two dimensional gel electrophoresis and proteins identification ....................... 47 

3.1.3.3. Influence of the ligands chemistries and average protein properties .................. 55 

3.1.3.3.1. Average hydrophobicity (AH) ............................................................................. 55 

3.1.3.3.2. Average polarity (AP) .......................................................................................... 59 

3.1.3.3.3. Average flexibility (AF)....................................................................................... 62 

3.1.3.3.4. Average Bulkiness (AB) ...................................................................................... 65 

3.1.3.3.5. Average surface hydrophobicity (ASH)............................................................... 66 

3.1.3.4. Comparative studies of the hydrophobic ligands based on average protein 

properties .......................................................................................................................... 68 

3.1.3.5. Application note .................................................................................................. 70 

3.1.4. Partial conclusions ...................................................................................................... 71 

3.2. Influence of the different base support chemistries during hydrophobic interaction 

chromatography ........................................................................................................................ 72 

3.2.1. Summary .................................................................................................................... 72 

3.2.2. Chemistry of the base supports .................................................................................. 73 

3.2.3. Results and Discussion ............................................................................................... 75 

3.2.3.1. Chromatographic fractionation ........................................................................... 75 

3.2.3.2. The electrophoretic separation and proteins identification ................................. 78 

3.2.3.3. Influence of the base support chemistries and average protein properties .......... 85 

3.2.3.3.1. Average hydrophobicity (AH) ............................................................................. 85 

3.2.3.3.2. Average polarity (AP) .......................................................................................... 89 

3.2.3.3.3. Average surface hydrophobicity (ASH)............................................................... 93 

3.2.3.3.4. Other protein properties affecting the chromatographic behavior ....................... 95 

3.2.3.4. Comparative studies of the different base supports based on average protein 

properties .......................................................................................................................... 98 

3.2.3.5. Application note ................................................................................................ 100 

3.2.3. Partial conclusions .................................................................................................... 101 

3.3. Protein distributions on 2-D gels: the influence of molecular weight and isoelectric point 

coordinates ............................................................................................................................. 102 

3.3.1. Summary .................................................................................................................. 102 

3.3.2. Results and Discussion ............................................................................................. 103 

3.3.2.1. Unfolding of proteins during HIC ..................................................................... 103 

3

3.3.2.2. Protein distributions on 2-D gels ....................................................................... 104 

3.3.2.3. The effect of protein molecular mass on the chromatographic behavior .......... 106 

3.3.2.3. The influence of protein isoelectric point on the chromatographic behavior ... 108 

3.3.3. Partial conclusions .................................................................................................... 111 

4. Conclusions and Remarks .................................................................................................. 113 

4.1. General conclusions and remarks ................................................................................ 113 

4.2. Influence of different ligands ...................................................................................... 113 

4.3. Influence of different base supports ............................................................................ 113 

4.4. Effect of protein properties on separation behavior during chromatography ............. 114 

4.5. 2-D gels analysis ......................................................................................................... 115 

4.5. Additional conclusions ................................................................................................ 115 

4.6. Recommendations for future work .............................................................................. 116 

5. References .......................................................................................................................... 117 

4

Chapter 1 

INTRODUCTION 

5

Chapter 1 

1. Introduction 

1.1. Introduction to Biotechnology 

The term biotechnology is a fusion of biology and technology which is concerned with the 

utilization of biological agents to produce useful products of human use (1). Biotechnology 

has been known for thousands of years, where humans have used it for the food purposes e.g. 

for the selective breeding of crops and livestock, production of wine, beer, cheese and other 

dairy products. In the early twentieth century, a new era of the modern biotechnology started 

with the production of complex organic molecules like antibiotics, vaccines and monoclonal 

antibodies. The production of these macromolecules has been optimized by the improved 

fermentation procedures and novel downstream processing approaches (2). The discoveries 

for the new products have made the pharmaceutical industry as one of the fastest growing 

sectors in the global economy. The pharmaceutical, agrichemical and biotechnology related 

products have the billions dollar annual sale in market excluding the food and beverages (3, 4). 

Proteins and enzymes are the main biopharmaceutical products and have several applications 

in food and medical biotechnology (5-7). The production of human biopharmaceuticals was in 

limited supply and very costly before the recombinant DNA technology revolution. The 

proteins were commonly used before from the plant's and the animal's origin directly which 

was mostly not feasible due to the resulting immune responses against foreign antigens. 

However, genetic engineering made it possible to clone the responsible genes in microbial 

cells and produce the larger amount of protein of interest. Bioprocess can be usually divided 

into two main parts such as upstream and downstream processing. Upstream processing is the 

successful fermentation step and downstream processing is the purification plan of the desired 

protein at a large scale. Although a lot of work has been done for the fermentation 

optimization and development of recombinant strain producing the biomolecules, but the 

purification process is still a major bottleneck in order to fulfil the market demands. The main 

1

Chapter 1 

objective is to obtain the highest yield and purity of the target protein in the minimum 

possible resources by downstream processing (8). 

1.2. Downstream processing 

Purification is the next step after the production of a biomolecule through successful 

fermentation. The isolation and purification of a biomolecule at industrial level to a form, 

suitable for its intended use is called downstream processing. The products of biotechnology 

include whole cells, organic acids, amino acids, antibiotics, industrial enzymes, gums, 

vaccines etc. The different separation methods can be applied for the recovery and 

purification of the biomolecules depending on their sizes and natures (2). The purification of 

the desired product needs certain downstream processing steps, which accomplishes 60% of 

the total cost, excluding the cost to purchase raw materials (9, 10). Several expert systems 

have been developed for the protein purification processes based on the use of empirical 

knowledge that is not exercised in nature and is typically used by experts (11-14). The 

number of steps varies ranging from eight to fourteen in an expert system of common practice. 

Additional steps will cause an increase in the cost and also a decrease in the yield. As the 

number of steps for any downstream process is increasing, the product loss will occur. For an 

efficient downstream processing, it is important to design a bioprocess with the minimum 

possible steps. This will increase the yield and purity of the product to an acceptable range 

(15-18). The bioprocess has usually two main steps; recovery and purification. Recovery is 

composed of a collection of cell proteome from disrupted cells, while purification is the 

chromatographic fractionation of the cell proteome for the isolation of desired protein. The 

target protein has to be isolated from other impurities such as DNA, RNA and cellular debris, 

if the product exists intracellular. Minute amount of other impurities can be separated by 

different chromatographies to avoid any toxic effects in the final product coming from a 

biological mixture (8). It is also important to study other contaminants available in the host 

2

Chapter 1 

cell proteome to facilitate the separation of the target protein in the real bioprocess. Several 

expert systems have been proposed to link the efficiency of a unit operation with the protein 

properties and to separate the target protein from the other discrete number of contaminants. 

Most of these approaches to understand the chromatographic behavior of proteins were 

limited to the use of model proteins instead of protein mixtures in the host cell proteome. No 

comprehensive struggle has been made before to define the contaminant proteome of real 

recombinant host in order to investigate the individual contaminant behavior during its 

chromatographic separation. A paradigm shift from focussing on protein product to defining 

the major contaminant might give a more realistic picture and could also make this approach 

more suitable. The characterization of host related proteins is also important to maintain the 

product quality (9, 19). 

Chromatography is an important unit operation of any downstream process which helps to 

remove the contaminants during the purification of biological materials. Chromatographic 

separation of protein mixtures is becoming one of the most effective and widely used means 

of purifying individual proteins. The chromatographic methods for protein separations are 

based on the general physicochemical properties of the proteins (Figure 1) (20). Minor 

differences between various proteins such as size, charge and hydrophobicity can be used to 

purify one protein from the other proteins during chromatography. Chromatographic 

separations are usually based on protein characteristics such as charge (ion exchange 

chromatography), size (size exclusion chromatography), isoelectric point (chromato focussing) 

affinity (affinity chromatography) and hydrophobicity (hydrophobic interaction 

chromatography and reverse phase chromatography). Hydrophobic interaction 

chromatography (HIC) is a chromatographic method based on protein hydrophobicity (4). 

HIC was considered as more significant than other chromatographic methods, due to its mild 

hydrophobic nature and feasibility for the separation of less stable proteins (21). HIC is a 

3

Chapter 1 

chromatographic method of choice in downstream processing and have been widely used for 

the purification of proteins, enzymes, antibiotics and vaccines (14, 22, 29). 

Figure 1: The chromatographic methods based on different protein properties (Adapted from 

Ref. 20). 

1.3. Hydrophobic interaction chromatography (HIC) 

In industrial biotechnology, the purification of the desired product is very important after 

successful fermentation. There are several chromatography methods available for protein 

purification based on the physicochemical properties of the proteins. However, HIC can be 

preferred due to its ability of rapid separation, high resolution and less chances of protein 

denaturation (23, 24). HIC is based on the reversible interactions between the hydrophobic 

surface patch of a protein and the hydrophobic surface of a chromatographic medium at 

moderately high salt concentrations. Proteins contain a variety of amino acids having 

hydrophilic and hydrophobic residues. Many of the hydrophobic residues are buried in the 

interior of the proteins, but on exposure to a solvent, a significant proportion of them can 

appear on the surface. However, the ratio of surface hydrophobicity differs in different 

4

Chapter 1 

proteins. It is this difference which can be exploited in HIC for protein separations. Protein 

hydrophobicity is the main physicochemical property which has a decisive role in retention 

during chromatography. There is no universally agreed single method to calculate protein 

hydrophobicity, however there is a general agreement that it can be determined by the 

hydrophobic contribution of the amino acid residues (25, 26). The operating conditions such 

as mobile phase properties (salt type, ionic strength and pH), stationary phase characteristics 

(chemical nature of the backbone, type of hydrophobic ligand and substitution level of the 

resin) and temperature play important roles in HIC (27, 28). Hydrophobic interactions are the 

most important non covalent forces which have the ability to maintain the native structures of 

the proteins at high salts concentrations. The basic principle of the HIC is different from size 

exclusion and ion exchange chromatography (IEC) and thus can be used for the products 

which have no possibility to be separated by these techniques. HIC is preferred over other 

chromatographic methods if proteins of similar size or isoelectric point coordinates have to be 

separated from each other. HIC can also be used as an ideal step, after the separation of 

biomaterials during IEC at high salt concentrations. Due to the entries of several new HIC 

media and better understanding of the factors affecting the retention, HIC has become one of 

the most widely used method for purification of proteins such as serum proteins, membrane 

proteins, nuclear proteins, receptors and recombinant proteins for research and industrial 

applications (14, 23, 29). The enzymes of commercial importance such as lipases, amylases, 

streptokinases can be purified utilizing HIC (30). Several expert systems have been produced 

to exploit the physicochemical properties of the proteins in order to separate the desired 

product from other contaminants in HIC (31, 32). In most of these approaches, the prediction 

of protein retention was limited to the use of model proteins instead of the host cell proteome 

(33). Studies of the host cell proteome which produces the product of interest can help in the 

design of the process; give in depth knowledge of the target and undesired proteins for the 

efficient downstream processing. No large scale effort has been made to study the 

5

Chapter 1 

contaminants which have close similarity to the target protein in the host cell proteome (19). 

In order to do so, one has to design the experiments to analyze the adsorption affinity of the 

adsorbents with a natural host. 

The first report about HIC was done by Sheppard and Tiselius observing the retention of dye 

in the presence of sulfate and phosphate solutions and was called salting out chromatography 

(34). Afterwards, Shalteil and Er-el used the term hydrophobic chromatography, Hofstee 

named the method as hydrophobic adsorption chromatography (34, 35). Finally, Hjerten in 

1973 described the method as HIC (36). Porath et al. discovered that hydrophobic adsorption 

was enhanced by using salts like sodium chloride or phosphate chloride and named the 

method as salt promoted adsorption chromatography (34). 

A typical chromatogram obtained during my experiments has been presented in Figure 2. To 

facilitate the hydrophobic interactions and adsorption of proteins, it is important to load the 

proteins on a column with a buffer of high salt concentrations, followed by elution with a 

linear or a stepwise decrease in the salt concentration (37). Linear gradient can be applied 

during chromatographic separation of biological mixtures to separate the proteins of diverse 

nature. After sample application, the proteins with no binding affinity will elute in flow 

through and washing step. The proteins bound with the adsorbent will elute according to their 

binding capabilities and the extent of protein hydrophobicity. The proteins with less surface 

hydrophobic residues will elute at the start of the chromatography. In contrast, the proteins 

with high hydrophobic residues on the surface will be tightly bound and will elute at the end 

of the chromatography. The highly hydrophobic proteins are usually tightly bound to the 

column and can be removed by salt free buffer or 20% ethanol. As the surface hydrophobicity 

of the proteins is increasing, the retention time of the proteins will increase accordingly 

during chromatography. 

6

Chapter 1 

Salt conc. decreasing 

(Gradient elution) 

Sample application. 

Abs. 

280 nm 

Unbound proteins 

elute before gradient. 

Tightly bound 

proteins elute in salt 

free conditions 

Column Volume (CV) 

Figure 2: A typical HIC chromatogram describing the chromatographic fractionation during HIC. 

1.4. Factors affecting protein chromatography behavior in HIC 

1.4.1. Effect of mobile phase parameters 

The mobile phase parameters affecting the protein retention are usually ionic strength, type of 

salts and buffer pH. Protein adsorption on hydrophobic adsorbents is usually favored by 

moderately high salt concentrations. The concentration of the salt should be according to the 

binding strength of the proteins and adsorbents and below the concentration causing 

precipitation of proteins. The optimum concentrations of salts are usually between 0.75 M and 

2.0 M with ammonium sulfate and 1.0 M to 4.0 M with sodium chloride (27). The different 

types of salts can be divided into kosmotrophic and chaotrophic salts depending on their 

ability of hydrophobic interactions. The chaotrophic salts (magnesium sulfate and magnesium 

chloride) have less capability to bind water molecules which increases the inclusion of water 

molecules on the protein and ligand surface and thus decreases hydrophobic interactions 

7

Chapter 1 

(salting in effect). While kosmotrophic salts (sodium, potassium or ammonium sulfate) bound 

tightly to water molecules which facilitate the exclusion of water molecules on the proteins 

and ligand surface and thus promotes hydrophobic interactions (salting out effect) (Figure 3) 

(20, 38). 

Figure 3: The mechanism of hydrophobic interaction chromatography (Adapted from Ref. 20) 

It has been reported by Hjerten that the main force which arises during the dispersion of water 

molecules is an increase in the entropy of the system. It has been stated that the hydrophobic 

ligand-protein interaction was thermodynamically favorable process (36, 39). The Hofmeister 

(lyotrophic) series of cations and anions was proposed before, starting with those favoring the 

interactions to those that disfavor the hydrophobic interactions. For anions and cations, the 

series is given below. 

Anions; PO 4 

3- 

> SO 4 

2- 

> CH 3 COO - > Cl - > Br - > NO 3 

- 

> ClO 4 

- 

> I - > SCN - 

Cations; NH 4 

+ 

> Rb + > K + > Na + > Li + > Mg 2+ > Ca 2+ > Ba2 + 

8

Chapter 1 

The ions at the start of the series are called kosmotropes or anti-chaotropes and have been 

reported to have stronger interactions with water molecules and promote hydrophobic 

interactions (40, 41). The effects of the salts on protein retention can also be explained by the 

number of water molecules released by the induction of different types of salts. The 

selectivity of the different salts can also be predicted by the differences in their capability to 

exclude water molecules from proteins and adsorbent surface (27). 

The mobile phase pH is another factor affecting the protein adsorption during hydrophobic 

interaction chromatography. At highly basic pH values (up to 9-10), a decrease in 

hydrophobic interactions between proteins and adsorbent occur, due to the changes in the 

hydrophilicity of proteins. In contrast, hydrophobic interactions increase by decreasing the pH 

of the mobile phase. The proteins which are usually not able to bind at neutral pH will have 

more chances of binding at acidic pH values (42). The basic proteins, such as lysozyme (pI = 

10.7) has shown longer retention at basic pH values. In contrast, acidic proteins, such as 

human serum albumin (pI = 5.2) has revealed less retention during chromatography at basic 

pH values (43). In another report, the total number of releasing water molecules were found 

higher, when the mobile phase pH was close to the pI of the protein and decreased when pH 

was away from the pI (44). The effect of pH has been rarely reported and the reason could be 

the instability of the proteins and adsorbents at elevated pH conditions. Another study also 

stated that an optimal pH should be maintained in HIC to obtain high purity and yield at their 

maximum extent (45-48). The temperature was also reported to have an effect in HIC. It has 

been observed that increasing the temperature promoted protein retention and lowering the 

temperature enhanced protein elution. At higher temperature, the proteins are usually 

denatured and hydrophobic residues come on surface which resulted in high hydrophobic 

interactions. Due to the higher chances of unfolding at elevated temperatures, the unstable 

proteins should be separated at low temperatures. The proteins purified in the cold room may 

not be reproducible at the room temperature (49, 50). All the reports for the effects of these 

9

Chapter 1 

factors were mostly studied with model proteins; however their applicability to a natural host 

still needs to be explored. 

1.4.2. Effect of stationary phases in HIC 

Stationary phases are usually composed of small hydrophobic groups (e.g. butyl, phenyl and 

hexyl) attached to the hydrophilic polymer base supports. The different types of stationary 

phases in HIC differ in the degree of ligand substitutions on the base support. The base 

supports can also differ in the chemical nature and particle size (51). The availability of the 

hydrophobic adsorbents is increasing in the market, due to the increasing applications of HIC 

as a purification method. The adsorbents determine the protein adsorption selectivity in HIC 

according to the nature of ligands and base supports. The straight chain alkyl ligands were 

reported for pure hydrophobic character while aryl ligands (phenyl) showed mixed mode 

behavior due to the presence of aromatic interactions in addition to the hydrophobic 

interactions (52-55). The polyether’s ligands such as polyethylene glycols and polypropylene 

glycols were also reported for their milder hydrophobic nature than alkyl ligands (56). The 

retention of cytochrome c, ribonuclease A, lysozyme and ovalbumin has been reported on 

polyvinyl alcohol, oligoamino alcohol, and polyoxyethylene silica supports in descending 

gradient of ammonium sulfate buffer. It has been observed that hydrophobic interactions 

between proteins and adsorbents increased with increasing chain lengths of the ligand (55, 57). 

However, resolution of the chromatography decreased when ligands of the higher chain 

lengths have been used (51). The effects of adsorbent hydrophobicity on the conformation of 

proteins were also reported. The elution profiles of lactalbumin have shown different behavior, 

when separated by polyether stationary phases having methyl and ethyl terminating groups. 

The lactalbumin has given one and two peaks for the adsorbents with methyl and ethyl 

ligands, respectively. The first peak was the native one while the second peak was the 

denatured peak of the lactalbumin. The second peak has been retained longer due to the 

10

Chapter 1 

exposure of hydrophobic residues usually buried in the interior of proteins. Other protein such 

as lysozyme when applied has shown single peak on both adsorbents due to the stable nature 

of the protein (46). The charged type HIC adsorbents have shown different behavior during 

chromatographic separations. Ionic interactions will occur between proteins and adsorbents in 

the presence of charges, which favor the use of charge free adsorbents in HIC. The choice of 

ligand type is hard to interpret and could be established case to case basis depending on the 

biomaterials to be separated (53, 54). The retention behavior of the proteins can be changed 

by adjusting the ligand substitution on the base support. The protein binding capacity has 

been increased by an increase in the substitution degree of the ligand. Once the degree of 

substitution reaches beyond the sufficient level, the binding capacity of proteins remains 

constant, although the strength of interaction increases. At very high substitution of the 

ligands, multipoint attachments occur between proteins and adsorbents resulting in stronger 

binding. Thus harsh conditions are required to elute the product which may also increase the 

chances of protein denaturation. Some studies have preferred low ligand density in case of the 

unstable proteins to preserve the native structures of proteins (58-60). 

There are different types of base supports such as hydrophilic polysaccharide gels (e.g., 

agarose and dextran), synthetic polymers (polymethacrylate and polystyrene divinyl benzene) 

or modified silica gel commonly used in HIC. It has been recently investigated with model 

proteins, that the adsorbent chemistry and up to some extent its particle size have effects on 

the retention behavior during chromatography (61, 62). The total number of water molecules 

released upon adsorption was found proportional to the hydrophobic area of the adsorbent 

(44). All the above findings were based on the retention data of model proteins and have been 

never investigated with the process proteomics approach. 

11

Chapter 1 

1.4.3. Effect of protein properties in HIC 

There are several protein properties affecting the chromatographic separation of proteins in 

HIC. However, the most important is protein hydrophobicity (51) and specifically average 

surface hydrophobicity (63) which has a decisive role in HIC. The protein hydrophobicity can 

be either the hydrophobicity of the exposed amino acid residues of a protein called as 

“average surface hydrophobicity” or depending on the hydrophobicity of the exposed and 

buried residues of a protein called as “degree of hydrophobicity” (27). The protein 

hydrophobicity has the main role in HIC which relies on no absolute definition. There is a 

general agreement that protein hydrophobicity can be the combined hydrophobic 

contributions of all the amino acids in a protein sequence. The hydrophobic potential of the 

various proteins can be derived by utilizing several amino acid hydrophobicity scales. Several 

experimental and theoretical approaches have been used by different researchers to assign the 

hydrophobicity value to each individual amino acid (25, 31, 64, 65). There are several 

hydrophobicity scales available in open literature which can be used for the determination of 

protein hydrophobicity. Tanford is one of those scales based on the transfer of free energies of 

the amino acid side chains from an organic environment to an aqueous environment (25, 66). 

Proteins when applied in an aqueous solution, the hydrophilic amino acid residues such as 

serine, threonine, asparagines, and glutamine tend to be exposed on the surface while most of 

the hydrophobic amino acid residues such as alanine, valine, leucine, isoleucine, 

phenylalanine, tryptophan, and tyrosine will be buried in the interior of protein molecules. 

However, adsorption of proteins occurs based on binding of the surface hydrophobic patches 

of a protein on the adsorbent. Several methods have been reported to quantify “protein surface 

hydrophobicity”. The first one was based on the binding of fluorescence probes to the 

hydrophobic region of a protein which has given rise to an increase in fluorescence according 

to protein hydrophobicity. Due to the possibility of ionic interactions in case of the anionic 

probes, this methodology was considered as non-applicable (64). The second method was 

12

Chapter 1 

based on the surface hydrophobic residues in a protein three dimensional structure (67). In 

addition to protein hydrophobicity, there are other physicochemical properties of the proteins 

such as average flexibility, average polarity, molecular weight and charge which have been 

rarely used for their effects in HIC. The average flexibility was reported to have a direct 

relationship with retention time of the model proteins in HIC. The proteins with low 

flexibility have been reported to be rigid towards conformational changes during 

chromatographic separations. In contrast, the protein with high flexibility will have more 

flexible nature towards conformational changes and when they come in contact of HIC 

surface, it will unfold or expose the internal surface in order to increase hydrophobic 

interactions and result in longer retention during chromatography (33, 62). The average 

polarity of the proteins was rarely reported to have an effect on protein retention in HIC. It 

was reported before that there is no obvious relationship between retention time and 

isoelectric point of the individual proteins (68). The effect of molecular mass on the retention 

behavior of proteins has been investigated before in ion exchange chromatography (19). 

However, there are very few reports where the effect of protein molecular weight on the 

chromatographic separation has been observed in HIC (33). There are few reports available 

where the above mentioned properties have been studied in HIC. However, these properties 

have been never researched for their effects on the chromatographic separations of a host cell 

proteome during HIC. 

1.4.4. The comparison between HIC and RPC 

The protein adsorption on hydrophobic adsorbents is generally weaker than reported in 

reverse phase chromatography (RPC), although both methods were based on hydrophobic 

interactions. In RPC, the protein-adsorbent interaction is so strong that protein elution can 

only be exerted by using organic solvents as elution buffer. The use of organic solvents and 

strong hydrophobic interactions are usually resulting in irreversible denaturation of proteins. 

13

Chapter 1 

This is also detrimental to the native structure and biological activity of proteins (69). In 

contrast, HIC is based on mild hydrophobic interactions and have importance to maintain the 

native structures and biological activity of the target proteins. The protein retention time can 

be predicted in HIC based on the surface residues of a protein. However, due to the 

denaturation of proteins in RPC, the elution is based on the exposed and buried hydrophobic 

residues of a protein (70). In other words, the protein retention in RPC can be estimated from 

the primary structure of a protein (71). In Table 1, the comparison between HIC and RPC has 

been given based on some general features (72) . There are several reports where they have 

used RPC for the purification of peptides. Several models have been proposed for the 

prediction of peptide retention time (73, 74). Although RPC has been widely used in the 

purification of peptides, however the application of RPC to large polypeptides and proteins 

has seen a significant increase in the last decade. The prediction of protein retention time has 

been reported based on the hydrophobicity and polypeptide chain length (75). However, 

further improvement to design prediction models is highly needed to be investigated. 

Table 1: The comparison of HIC and RPC (Adapted from Ref. 72). 

Description HIC RPC 

Stationary phases Low ligand density High ligand denisty 

Hydrophobic conditions Mild hydrophobic conditions High hydrophobic conditions 

Elution buffer Decreasing gradient of salts Increasing gradient of 

organic solvents 

Conformational changes Low conformational changes High conformational changes 

Protein retention 

Based on exposed 

Based on exposed and buried 

hydrophobic residues 

hydrophobic residues 

14

Chapter 1 

1.5. Analysis of the yeast proteome as biotech feedstock 

The proteomics tools such as two dimensional gel electrophoresis (2-D PAGE) and MALDI- 

ToF-MS are becoming the prominent methods for the analysis of the cell proteomes of 

various organisms. The proteomics analysis is usually the electrophoretic separation of 

proteins using 2-D PAGE followed by protein identifications (106). These proteomics studies 

are important to design the purification strategies and bioprocesses for the recombinant 

proteins. The bioprocesses are usually step by step approaches such as protein extraction, 

concentration, chromatographic separations and polishing. Several bioprocesses have been 

developed using different host cells and chromatographic methods for the purification of 

target proteins (109-110). Previously, master step purification was reported for the separation 

of Haemophilus influenzae cell proteome by heparin chromatography followed by 

electrophoretic separations and identification of proteins (76). The master step purification 

has gained more interest due to complete genome sequencing of several microorganisms of 

industrial interest and with the additional new improvements in the 2-D PAGE and MALDI- 

ToF-MS. The studies of the host cell proteome expressing the recombinant proteins called 

expression systems are important for protein purification. The expression systems can have 

the advantage of producing the maximum amount of target proteins in comparison to natural 

systems. There are few expression systems for the recombinant protein production such as 

bacteria, plants, animals, and yeast cells, usually Saccharomyces cerevisiae and Picchia 

pastoris (77). The cell proteomes of several microorganisms such as Candida albican, 

Plasmodium falciparum and Haemophilus influenzae has been reported by 2-D PAGE 

technology (78, 112). The Saccharomyces cerevisiae was selected as a host cell proteome due 

to several advantages over other expression systems such as high cell growth, high protein 

expression ratio, post translational modifications and most importantly it was the first 

organism to have its genome completely sequenced. The crude extract of the yeast cell 

15

Chapter 1 

proteome has been studied before using 2-D PAGE and mass spectrometry (105). However, 

the yeast cell proteome has been rarely investigated for its separation behavior on 

hydrophobic adsorbents by HIC. Hence, a study was planned to get a deeper insight into S. 

cerevisiae cell proteome and its separation behavior during HIC. The yeast cell proteome was 

chromatographed on different hydrophobic adsorbents and the fractions were collected to 

further investigate the proteins available in each chromatographic fraction. In a previous 

report, a three dimensional protein characterization approach has been applied to a complex 

plant extract using aqueous two phase system instead of HIC (112). The separation of yeast 

cell proteome was also three dimensional (3D) based on hydrophobicity (HIC), followed by 

its separation based on isoelectric point and molecular weight (2-D PAGE). Thus, the 

chromatographic and electrophoretic separation of the proteome was mainly based on three 

basic properties of the proteins, such as molecular weight, isoelectric point and 

hydrophobicity. 

1.6. Mass spectrometry and database searching 

Mass spectrometry is an important step in most of the proteome wide approaches to 

understand the biological processes. During the last decade, the genomes of most of the 

organisms of biological importance have been sequenced for the better understanding of 

genomes and proteomes. Once the genome sequence information was collected, the paradigm 

has shifted from sequencing to identification. The current paradigm was to investigate the 

host cell proteomes through different proteomics tools such as 2-D PAGE and mass 

spectrometry. The poteins can be identified either directly from the gels or through gels free 

approaches from the mixtures in solution (Figure 4) (79). The tandem mass spectrometry and 

MALDI-ToF-MS can be used from gel free and gel based protein identifications, respectively. 

The gel based identification approach has been reported many times and have several 

advantages over gel free approach (106, 109, 111). The gel electrophoresis can remove low 

16

Chapter 1 

molecular weight impurities and buffer ions during electrophoresis for which the MALDI- 

ToF has more sensitivity. Another advantage of the gel based approach is that polyacrylamide 

materials act like safe containers to handle and excise very small amount of the proteins. The 

protocols for the gel based approach are well established in comparison to gel free approach 

(80, 81). The mass spectrometers consist of three essential parts such as ionizer, analyzer and 

detector. The ionization source converts molecules into gas-phase ions. This ionization is 

facilitated by the energy provided by laser. As ions exit the ion source, the individual mass to 

charge (m / z) ratios are separated by a second device, a mass analyzer. The mass analyzer 

separates ions on the basis of mass to charge ratios. In MALDI-TOF-MS, the molecules are 

given mass values based on their movement or time of flight in the drift tube. Lighter ions fly 

faster than heavier ions and when it reaches to the third part of the mass spectrometer called 

detector. The signals are recorded on the detector and a mass spectrum is obtained. The 

detector records the charge produced during the hitting of ions on the surface. The spectrum is 

produced based on mass charge values. The experimental masses can be then compared to the 

theoretical masses available in the proteome of specific organism (80). The peptide mass 

fingerprinting approach can be used for searching protein databases. The principle behind 

PMF is very simple; the identified protein is the protein has the best match between the 

experimental and theoretical mass data. A theoretical digest of all the proteins sequences in 

the database by a specific enzyme can be used for predicting the individual peptides. These 

theoretical peptides can be searched to match peptides derived from the experimental mass 

data and then ideally one protein has to match the protein of interest. The protein will be 

considered as identified if the score is greater than the score fixed by Mascot with a p value 

less than 0.05. In addition, the experimental and theoretical values of the molecular weight 

and isoelectric point of a protein must be in the similar range. The databases such as NCBI 

and Swiss Prot can be searched for the identifications of proteins (82). 

17

Chapter 1 

Figure 4: The schematic presentation of protein identification using mass spectrometry 

(Adapted from Ref. 79). 

1.7. The in silico Approach 

The term in silico is actually the computational models or simulations based on the 

experimental data which can be used to make hypothesis and to predict about any biological 

process. The in silico approach has been widely used in pharmacology to make predictions, 

suggest hypothesis and ultimately leads towards new findings in drug designing and 

therapeutics. The in silico approach have the capability to increase the chances of discovery 

and decrease the laborious work in the laboratory. Many discoveries in the pharmacology 

were made by using the in silico approaches (83, 84, 85). It was stated that successful 

industrial companies are those that collect information from their experiments and introduce 

some rules that can show the pathways to obtain the target product for the clinical analysis 

18

Chapter 1 

followed by its introduction into the market. It has been reported that in silico approaches can 

help to make decisions and simulate about certain pharmaceutical processes which can further 

reduce the space between pharmaceutical and engineering based disciplines (84). To develop 

the computational models, it is necessary to have enough experimental data in the databases. 

These computational models can be exploited to predict about any biological process. The in 

silico approach has been reported to play its role as a complement to in vitro and in vivo 

experimentation (83). The upstream and downstream processes need to be improved utilizing 

the in silico approaches. The advances in the upstream processes such as fermentation have 

been already achieved and a product can be produced in several grams instead of milligrams 

due to the introduction of genetic engineering technologies (85). However, the major 

bottleneck still exists in the downstream processing of bioproducts. The current lack of the 

accurate data about the bioseparation of the products and the structural implications of the 

bioproducts are the main hurdles to design the purification models. A downstream approach 

has been proposed to collect the experimental data to design models for the improvement of 

the in silico approach (Figure 5). To collect the experimental data using modern mass 

spectrometry based proteomics tools after crude fractionation are highly required to design 

efficient bioprocesses (86). However, the in silico approach has been rarely reported in the 

downstream processing of proteins and the reason could be the lack of information with the 

real expression systems. Mostly model proteins were used to produce data and that data was 

then used to predict about the retention behavior of the other model proteins using 

mathematical models. The next step was then to compare the experimental retention time with 

that of predicted ones. In case of deviations from the predicted one, certain parameters have to 

be changed accordingly to calibrate the computer model. Once the model has been calibrated, 

no further investigations are required and the researcher can simulate the separation behavior 

of proteins on computer. 

19

Chapter 1 

Figure 5: Flow chart describing the fractionation and characterization procedure of the crude 

extract for obtaining the data about bioprocess parameters. The data produced can be stored 

in databases to design the purification models (Adapted from Ref. 86). 

Several researchers have reported different methodologies to predict about the retention time 

of model proteins (15, 87). However, the prediction models were based on the data collected 

from very few experiments and due to this reason there is still no universal scheme which can 

be considered as the best one for the downstream processing. These models have several 

drawbacks which can raise questions on their applicability. A major drawback was that the 

experimental data for these models has been derived from the model proteins instead of a host 

cell proteome. Another major drawback was that the experimental data was based on the 

retention time and / or volume of the model proteins. This is not applicable to a cell proteome 

because at a certain time during chromatography, several proteins are going to be eluted 

instead of a single protein. The shortcomings in the previous in silico approaches have 

motivated our group to report a paradigm shift from focussing on the purification of a target 

20

Chapter 1 

protein towards purification of the main protein contaminants in a host cell proteome. The use 

of the cell proteome in relation with the chromatographic method will produce more authentic 

and realistic data and can be stored in databases. These databases can be used to generate the 

purification models for easy in silico downstream processing of proteins (19). Several 

conclusions have been made in that work although the approach was not complete in its 

essence. The mass spectrometry and computational methods were missing in that report and 

the data reported was not enough to design a model. In this work, a complete picture has been 

presented ranging from chromatographic fractionation of the yeast cell proteome to the 

characterization of proteins at molecular level. The proteins properties have been used to 

correlate proteins at the molecular level to their separation behavior during HIC. 

1.8. Goal of the work 

Complementing all the above discoveries made by several authors, the current work further 

progressed to a more comprehensive understanding of the hydrophobic adsorbents and their 

effects on the separation behavior of a cell proteome during HIC. The adsorbents are usually 

composed of ligands and base supports. The comparison of the adsorbents (different ligands) 

hydrophobicity has been reported before with model proteins. However, the adsorbents 

hydrophobicity has been never studied comparatively with a proteome wide approach. The 

reason could be the experimentally demanding and laborious approach which no one ventured 

to adopt for their studies. Therefore, this study was planned for the comparative analysis of 

the hydrophobic adsorbents as a function of yeast cell proteome and in relation to protein 

properties. 

Under the frame of the current research work, the following objectives were targeted. 

• To explore the influence of different hydrophobic ligands in HIC as a function of 

complex cell proteome. 

21

Chapter 1 

• To uncover for the first time the influence of different base supports in HIC with a 

proteome wide approach. 

• To explore the physicochemical properties of proteins for their correlation with 

chromatographic separation and ability to differentiate among different ligands and 

base supports. 

• To reconnoitre the contaminant profile of the yeast cell proteome after 

chromatographic fractionation using 2-D PAGE, mass spectrometry and 

bioinformatics. 

• To collect the experimental data and define certain operational windows, which can be 

annotated for the future implementation of the in silico downstream processing. 

22

Chapter 2 

EXPERIMENTAL APPROACH

Chapter 2 

2. Experimental approach 

2.1. Materials 

Yeast extract, peptone, dextrose, analytical grade buffer salts was purchased from Applichem 

(Darmstadt, Germany). The protease inhibitor cocktail was from Sigma-Aldrich Chemie 

GmbH (Steinheim, Germany). Acrylamide and dithiothreitol (DTT) were purchased from 

Carl-Roth GmbH (Karlsruhe, Germany). The Bradford Protein Assay Kit was purchased from 

Pierce Biotechnology Inc. (Rockford, IL, USA). ColorPlus pre-stained protein marker (New 

England Biolabs, Frankfurt, Germany) and N, N, N, N-tetramethyl-ethylenediamine (TEMED) 

were obtained from Serva (Heidelberg, Germany). The Toyopearl ® 

adsorbents were 

purchased from Tosoh Biosciences GmbH (Stuttgart, Germany). The C10/10 

chromatography column, chromatographic materials Sepharose-Phenyl, Source-Phenyl and 

pre-cast gels for electrophoresis were purchased from GE healthcare Europe GmbH (Munich, 

Germany). 

2.2. Experimental strategy 

The experimental strategy has been presented in Figure 6, which started with the cultivation 

of yeast cells followed by cells disruption. The cell proteome was collected after cell 

disruption and subjected to chromatographic fractionation using several hydrophobic 

adsorbents. The chromatographic fractions were collected in triplicate. The two dimensional 

gels were performed for the 42 fractions obtained during chromatography. Several proteins 

from each gel were selected for identification by MALDI-ToF-MS. Computational tools were 

used to calculate several properties of the proteins. Finally, the physicochemical properties of 

the proteins were correlated with the chromatographic behavior during HIC. All these 

experimental steps have been explained sequentially in the following text. 

24

Chapter 2 

Yeast cell proteome and chromatographic fractionation by HIC 

Phenyl ligated media (3) Polymethacrylate based media (3) 

21 fractions (7 each) 21 fractions (7 each) 

Perform 2-D PAGE for 

21 fractions separately 

Perform 2-D PAGE for 21 

fractions separately 

Identification of certain 

proteins from each gel 

by MALDI-ToF-MS. 

Identification of certain proteins 

from each gel by MALDI-ToF- 

MS. 

Bioinformatics approach 

Calculate several protein properties from the primary structure 

Average surface hydrophobicity calculated from three dimensional 

structure using MATLAB and STRIDE as computational tools 

Correlate protein properties with the chromatographic behavior and investigate 

the effects of ligands and base supports during chromatography 

Figure 6: Experimental strategy of the overall work. 

25

Chapter 2 

2.2.1. Cell Cultivation 

S. cerevisiae pBIVU02-1 (Biomedal, Sevilla, Spain) strain was streaked onto an YPD agar 

plate (1% Yeast extract, 2% Peptone, 2% Dextrose, 2% Agar) such that isolated single 

colonies will grow. The plates were incubated for two days. Then 30 ml of YPD liquid media 

was inoculated in a 250 ml flask with a single colony of cells from the YPD agar plate and 

grow overnight at 30°C with vigorous shaking (220 rpm) in the YPD liquid medium, pH 7.5. 

Then pre-culture was used to inoculate 300 ml YPD liquid media (1% yeast extract, 2% 

peptone, 1%w/v dextrose, pH 5.8) in 1.0 L baffled flasks. Cultivation was performed at 30°C, 

220 rpm, and for 48 hours. The cells were harvested by centrifugation in the late exponential 

growth phase at 3220 g for 15 minutes. The resulting cell pellet was washed three times with 

20 mM sodium phosphate buffer (pH 7.5) before cell disruption. 

2.2.2. Preparation of the crude extract 

Cell disruption was performed by bead milling at 4°C utilizing 0.5 mm glass beads. The cell 

paste (30 g) was suspended in 60 ml 20 mM sodium phosphate buffer (pH 7.5) and contacted 

by the grinding media for 30 min. One hundred micro litres of protease inhibitor cocktail (P 

8849; Sigma, St. Louis, MO, USA) was added per gram of cells. Cellular debris was 

separated by centrifugation at 3220 g for 25 min. The supernatant was filtered employing a 

0.45 µm membrane (Ministart Sartorius, Gottingen, Germany). The lysate was then ready to 

be used as a sample for chromatography experiments. 

2.2.3. Chromatographic fractionation 

Chromatographic fractionation was performed in an AKTA FPLC system equipped with a 

Frac-900 fraction collector and UNICORN 4.10 software for data collection and analysis (GE 

healthcare Europe GmbH, Munich, Germany). Two sets of chromatography stationary phase 

materials were used; one set had three different base supports (Sepharose-Phenyl, Source- 

Phenyl and Toyopearl-Phenyl) harbored the same ligand and the other set had different 

26

Chapter 2 

ligands (Toyopearl-Hexyl, Toyopearl-Butyl and Toyopearl-Ether) with same base support. 

Bed volume was 5.0 ml; the quality of the packing was evaluated by residence time 

distribution analysis using 1% acetone as a tracer (88). Mobile phases were prepared as 

follows: buffer A: 20 mM sodium phosphate buffer pH 7.5 containing 1.6 M (NH 4 ) 2 SO 4 , and 

buffer B: 20 mM sodium phosphate buffer. Buffers were filtered and degassed prior to use. 

After equilibration in buffer A (10 CV), the cell lysate (10 ml; 8.0 mg/ml) was applied to the 

column. The sample was introduced by a 10 ml super loop (GE health care, Uppsala, Sweden). 

Non-retained materials were washed out in buffer A (5 CV) and applying a linear gradient (25 

CV) from 0% to 100% of buffer B exerted elution. The flow rate was 1 mL / min. Fractions 

were collected utilizing the following salt concentration values as cut-off: 1 (1.6 M), 2 (1.4 

M), 3 (1.0 M), 4 (0.8 M), 5 (0.6 M), 6 (0.2 M) and 7 (0 M). The chromatographic experiments 

were performed in triplicate and the fractions were pooled before analysis. 

2.2.4. Membrane dialysis 

The materials collected in each of the seven fractions were further desalted by membrane 

dialysis (Pierce Rockford, IL, United States). The membranes (7 KDa) with samples were 

stirred in 5 litres bottle full of water. The salt buffer ions have been removed from the samples 

by diffusion. The dialysis was performed for two days in a cold room (4°C) with a renewal of 

fresh water after each 12 hours interval. After desalting, the samples were concentrated under 

vacuum (Vacufuge concentrator 5301, Eppendorf AG, Hamburg, Germany). 

2.2.5. Two dimensional polyacrylamide gel electrophoresis (2-D PAGE) 

The chromatography fractions were obtained as described earlier and dissolved in Lysis 

buffer (7 M urea, 4% w/v CHAPS, 2 M thiourea, 2% v/v Pharmalyte TM 3-10, and 1% w/v 

DTT). The samples were then centrifuged (3220 g, 15°C) to remove any insoluble material. 

Isoelectric focussing was performed using 7 cm Immobiline TM Dry strips pH 3-10, kept at 

20°C in a flatbed Multiphor II unit (GE Healthcare Europe, GmbH, Munich, Germany). The 

27

Chapter 2 

IPG strips were kept for 16 hours in rehydration buffer (6 M urea, 2 M thiourea, 1% CHAPS, 

0.4% DTT, and 0.5% v/v pharmalyte TM 3-10). The sample 200 µl (65 µg proteins in 75 + 125 

µl of rehydration buffer) was applied in in-gel rehydration buffer instead of cup loading. The 

sample was allowed to absorb on a strip for 15 min, and then covered with silicon oil. After a 

sixteen hour rehydration period, focussing was performed. IEF was performed on these 

conditions: 50 V, 120 min. 150 V, 90 min. 500 V, 60 min, 1500 V, 40 min. 2500 V, 40 min 

and 3500 V, 180 min at 20°C. The minute amount of buffer ions present in the samples was 

removed by applying low voltage (100 volts) at the beginning for 5 hours and the filter paper 

beneath the electrode (where the salt ions have collected) was changed time by time. After 

focussing, the strips were first equilibrated for 15 minutes in 10 ml equilibration buffer (50 

mM Tris-HCl, pH 8.8, 6 M urea, 30% glycerol, 2% w/v SDS and 10 mg/ml DTT) containing 

20 µl bromophenol blue and subsequently for additional 15 minutes in the same buffer 

containing 25 mg/ml iodoacetamide instead of DTT. After equilibration, the strips were kept 

on top of a 12.5% SDS vertical slab and covered by 0.7% hot low melting point agarose in 

SDS electrophoresis running buffer (25 mM Tris, 192 mM glycine, 0.1% SDS). The sodium 

dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed in a Hoefer 

mini VE vertical electrophoresis system in 1 mm thick 10 cm × 10.5 cm gels, 25 mA per gel 

and 120 volts (19). Colloidal coomassie blue was used for staining the gels (89). The gels 

were first fixed for at least three hours in the fixing solution (50% ethanol and 3% phosphoric 

acid). The gels tend to shrink during fixing. Then the gels were washed in deionized water 

three times for 20 minutes at a time. After washing, the gels were preincubated for 1 hour in 

staining solution (34% methanol, 3% phosphoric acid and 17% w/v ammonium sulfate). Then 

a small amount of coomassie blue was added with a spatula tip to the gels and stained 

overnight. After staining, the gels were washed with deionized water to remove the 

background stain. The gels were scanned by 48 bit Epson color scanner (Epson perfection 

4990 Photo). 2-D gels of the chromatography fractions were performed in duplicate and the 

28

Chapter 2 

number of spots was counted based on size and isoelectric point values. Certain spots were 

selected for in-gel digestion and protein identification. 

2.2.6. Protein content of the fractions 

The protein concentration in the fractions was determined by the Bradford protein assay kit 

(Pierce, Rockford, IL) as per the manufacturer's instructions. The protein content of the crude 

extract was measured before using it for chromatographic experiments. The protein content 

was also measured in all the chromatographic fractions collected during HIC. 

2.2.7. Protein identification by MALDI-ToF-MS 

Certain protein spots were manually excised from the 2-D gel for the identification purposes. 

In-gel digestion was performed according to the Shevchenko´s protocol (90) with some 

modifications. A high amount of salt was present in the samples, so an additional step was 

followed where a wash solution (50% v/v methanol and 5% v/v acetic acid) was added to gel 

pieces and kept overnight. The washing step was repeated three times. The wash solution was 

very effective in removing all kinds of impurities hindering the identification process. The 

other additional step was the use of 0.5% TFA to dissolve the dried peptides instead of 0.1% 

TFA mentioned in Shevchenko´s protocol. The reason was the presence of minute amount of 

buffer ions in the samples which was negatively affecting the spotting of proteins on the 

MALDI target plate. After washing with 0.5% TFA, the spots on the plate were solid and 

resulted in mass spectra of high quality. The modifications in the existing protocol should be 

considered in the protein identifications specific in case when HIC is involved. The overall ingel 

digestion process was performed in seven steps such as washing, de-staining, reduction, 

alkylation, trypsin digestion, extraction and concentration of the peptide extract (Table 2). 

29

Chapter 2 

Table 2: The in-gel digestion protocol optimized specifically for the proteins fractionated by 

HIC 

Steps 

Description 

1. Washing the gels The gel pieces were washed overnight, with 50% (v/v) methanol 

and 5% (v/v) acetic acids for the removal of salts and other 

impurities. 

2. De-staining After washing, the gel pieces were incubated in 200 µl of 100 

mM ammonium carbonate/acetonitrile (1:1 v/v) for 30 minutes 

to destain the gels. 

3. Reduction After destaining, 50 µl of 10 mM DDT dissolved in 100 mM 

ammonium carbonate were added to gel pieces and incubated 

for 45 minutes at 56°C to reduce the disulfide bridges. 

4. Alkylation Then 100 µl of 55 mM iodoacetamide was added to samples and 

incubated for 20 minutes in the dark to inhibit the reformation of 

disulfide bonds and alkylate the free cysteins. 

5. Trypsin digestion Then 10 µl of trypsin and 90 µl of 25 mM ammonium 

bicarbonate in 9% acetonitrile solution were added to each 

sample and incubated in ice for 3 hours to absorb maximum 

enzymes. The samples were then incubated for 16 hours at 37°C 

in the digestion buffer for protein digestion. 

6. Extraction of 

peptides 

7. Concentration of 

the peptide extract 

a. 200 µl of Milli Q water was added to each sample and 

incubated for 15 minutes at 37°C to extract hydrophilic 

peptides. 

b. 200 µl of extraction buffer (1:2 v/v 5% formic 

acid/acetonitrile) was added to each sample and 

incubated for 15 minutes at 37°C to extract hydrophobic 

peptides. 

The peptide extract was concentrated and 15 µl of 0.5% v/v 

TFA was added to each sample, followed by centrifugation at 

10,000 rpm for 10 minutes. The samples were then ready for 

identification by MALDI-ToF-MS. 

After digestion, the sample was collected as supernatant and 3 µl of the sample was spotted 

on the MALDI target plate followed by 0.5% TFA for washing. The sample was air dried at 

room temperature. All mass spectra were calibrated using peptides from the auto digestion of 

trypsin. The peak lists were searched using the Mascot search engine 

30

Chapter 2 

(http://www.matrixscience.com) against NCBI and SwissProt databases. The search 

parameters were carbamidomethylation and methionine oxidation entered as variable 

modifications and 1 missed trypsin cleavage site. In all protein identifications, the scores were 

greater than the score fixed by Mascot as significant with a p value < 0.05 (91). The identified 

proteins were further characterized and analyzed with computational tools. 

2.2.8. Characterization of proteins utilizing Bioinformics 

The same procedure has been followed for the calculation of ASH as described by J.C. 

Salgado et al utilizing the Cowan-Whittaker scale (32, 92). Average surface hydrophobicity 

(ASH) of the proteins was calculated by writing a package in MATLAB, where it was 

mandatory to use the three-dimensional structure of a protein collected from protein databank 

(PDB, http://www.rcsb.org/pdb). STRIDE was used to generate a text file, which can 

determine the accessible surface area (ASA) of each amino acid at a particular position. After 

calculating the ASA, the ASH was measured by the package in MATLAB, programmed with 

the normalized values of Cowan-Whittaker hydrophobicity scale. According to this method, 

the ASH can be calculated by equation 1 as follows; 

Φ = ˆ rφ 

(1) 

suface 

∑ 

i 

i∈A 

i 

Where 

Φ 

surface is the ASH of a protein, A is the collection of the 20 possible amino acids 

and ( φ i ) is the hydrophobicity of the amino acids of type i. The hydrophobicity of each amino 

acid was assigned according to Cowan-Whittaker scale. The fraction of superficial area ( r ∧ i) 

occupied by the amino acid i can be calculated by equation 2 as follows; 

31

Chapter 2 

∧ 

r 

i 

= 

Si 

S 

∑ 

jεA 

j 

(2) 

In equation (2), 

Si 

is the sum of the accessible surface area (ASA) for all the amino acids of 

type i. S j is the total surface area of the protein. The r ∧ i is the fraction of surface area occupied 

by amino acid i and A is the collection of 20 possible amino acids (93). The concept of this 

approach was based on the assumption that each amino acid on the protein surface contributes 

regarding its abundance, with the properties associated to the protein surface (32, 67). 

Average hydrophobicity (AH), average polarity, average bulkiness and average flexibility 

(AF) were calculated from the primary structure by using the scales available at Expasy 

ProtScale tool (94). The average hydrophobicity values were measured employing three 

different scales i.e. Tanford (T), Cowan-Whittaker (CW) and Miyazawa-Jernigan (MJ), 

respectively (25, 92, 95). The average bulkiness was calculated by the scale proposed by 

Zimmerman (96). The polarity values were calculated by adopting the Grantham’s and 

Zimmerman’s scales (96, 97). The flexibility indices introduced by Bhaskaran and 

Ponnuswamy were used to calculate the flexibility values of proteins (98). 

32

Chapter 3 

RESULTS AND DISCUSSION

Chapter 3 

3. Results and Discussion 

3.1. Influence of the different ligand chemistries during hydrophobic 

interaction chromatography 

3.1.1. Summary 

Several reports are available in literature in which various ligands have been compared based 

on the retention time of model proteins in hydrophobic interaction chromatography (HIC) (62, 

87, 99). However, the different ligands were comparatively investigated for the first time with 

the proteome wide approach. A proteome wide approach has direct industrial applicability and 

has several advantages over work performed with model proteins. This work is in the 

continuation of a previous report from our group, where the ion exchange beads were used to 

investigate the separation behavior of the insect cell proteome (19). Several conclusions were 

made in that report, although the approach was not complete in its essence. The mentioned 

approach was extended in this work for the comparative studies of the hydrophobic ligands 

employing some additional steps of mass spectrometry and bioinformatics; which were 

missing in the previous approach. This approach analyzed the chromatographic separation of 

the overall protein contaminants in the host cell proteome instead of focussing on any of the 

target protein. The Saccharomyces cerevisiae cell proteome was fractionated by different 

ligands during HIC. The chromatographic fractions were collected at specific salt 

concentrations, followed by their separation on two-dimensional polyacrylamide gel 

electrophoresis (2-D PAGE). Several spots were selected from each 2-D gel and subjected to 

identification by Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass 

Spectrometry (MALDI-ToF-MS). Later on, several protein properties such as average surface 

hydrophobicity, average hydrophobicity, average flexibility, average bulkiness and average 

polarity were calculated for the identified proteins using different bioinformatics tools. These 

properties were investigated for the first time with the proteome wide approach and correlated 

35

Chapter 3 

to the retention behaviors in HIC. These properties were also studied for their ability to 

differentiate among the three different ligands. In addition, this work was planned to collect 

the experimental data from the three ligands which can be used to design the purification 

model of recombinant proteins utilizing the in silico approach. 

3.1.2. Scientific background of the approach 

3.1.2.1. Hydrophobic adsorbents 

Hydrophobic interaction chromatography (HIC) is an important chromatographic technique 

used for the industrial scale purification of proteins. HIC was preferred over other techniques 

such as affinity chromatography, ion exchange chromatography and reverse phase 

chromatography, due to the mild hydrophobic interactions. These weaker interactions 

minimize the unfolding of the proteins and maintain the biological activities (68, 100). The 

hydrophobic adsorbents and protein properties are the two main parameters affecting the 

chromatographic behavior during HIC. The hydrophobic adsorbents are usually composed of 

hydrophobic ligands attached to the functional groups of the base supports. The hydrophobic 

ligands are usually ether, butyl, hexyl and phenyl groups depending on their chain lengths. As 

the chain lengths of the ligands are increasing, the water exclusion potential of the ligands is 

also increasing. In other words, the chain length of the ligand has a direct relationship to 

hydrophobic interactions. If the hydrophobic interactions between proteins and adsorbents are 

higher, the proteins will be tightly bound to the column and will elute at the end during 

chromatography. The hydrophobicity studies of the different ligands have been provided by 

the Vendor company and also reported in the open literature (62, 87, 99, 101). The ether 

ligand was reported to be the most hydrophilic and hexyl as the most hydrophobic ligand as 

presented in Figure 7 (99). 

Toyopearl-Ether < Toyopearl-Butyl < Toyopearl-Hexyl 

36

Chapter 3 

Ligand 

Base support 

(Toyopearl®) 

Figure 7: The chemistry and comparison of different ligands for their hydrophobicity based on 

model protein retention data (Adapted from Ref. 99). 

The ligands are usually attached to toyopearl ® 

base support by glycidyl ether coupling 

procedures. This procedure introduces a short spacer arm to minimize steric hindrance. At the 

start, HIC was a slow technique; rapid separation has been made possible by the introduction 

of micro particulate semi rigid supports (Toyopearl®) (101). This revealed the importance of 

toyopearl adsorbents at the industrial level. The physicochemical properties of the adsorbents 

are given in Table 3 (99). The three hydrophobic adsorbents of 65-100 µm bead sizes were 

selected for this study, which is the most suitable range for industrial scale purification. The 

hydrophobic charm of the ligands was narrated before with toyopearl as a common base 

support. However, the previous reports were based on the retention time of few model 

proteins and have been never studied with the biological mixtures. Hence, it was planned to 

37

Chapter 3 

investigate the hydrophobicity of the following three ligands as a function of the cell 

proteome and in relation to protein properties (Figure 8). 

Table 3: Physical properties of the adsorbents with different ligands (99) 

Adsorbents 

Base support 

Ligands 

Beads size 

(µm) 

Pore size 

(nm) 

DBC a (mg/mL) 

Toyopearl- 

Butyl 

polymethacrylate Butyl 100 100 

32 

Toyopearl- 

Hexyl 

polymethacrylate Hexyl 100 100 

33 

Toyopearl- 

Ether 

DBC a 

polymethacrylate Ether 65 100 

Dynamic binding capacity 

12 

Yeast proteome separation 

on hydrophobic adsorbents 

(HIC) 

Comparison of 

three different 

ligands 

Comparison of 

three base 

supports 

Protein 

characteristics 

Toyopearl-Hexyl 

Toyopearl-Butyl 

Toyopearl-Ether 

Source-Phenyl 

Sepharose-Phenyl 


Protein 

hydrophobicity and 

others 

Figure 8: The comparison of the three different ligands with common base support based on 

protein properties and as a function of the expression system. 

38

Chapter 3 

3.1.2.2. Protein properties in the context of HIC 

The physicochemical properties of the proteins such as average hydrophobicity, average 

polarity, average flexibility and average bulkiness can be determined by different scales 

proposed by several researchers (Table 4). These average properties were based on the 

exposed and buried amino acid residues of a protein. The average hydrophobicity is the 

average of the hydrophobicities of the exposed and buried amino acids of a protein. The 

average hydrophobicity of the proteins can be measured by the scales proposed by different 

researchers (25, 92, 95). Several experimental and theoretical approaches were used to 

determine the hydrophobicity indices for the individual amino acids. These methods have 

assigned different hydrophobicity values for each amino acid and can be used as different 

amino acid hydrophobicity scales. The scale of Cowan-Whittaker was derived from the amino 

acid retention times during chromatographic experiments by RP-HPLC (92). The scale of 

Miyazawa-Jernigan was based on the contact energies estimated from the protein tertiary 

structures. A direct relationship was observed between the contact energies of adjacent 

residues in the crystal structure and their corresponding hydrophobicities reported by Tanford 

(25, 95). The theoretical contact energies between amino acid residues in the crystal structures 

were used to assign the hydrophobicity value to each amino acid. The amino acid 

hydrophobicity scale proposed by Tanford was based on the amino acids solubility in water as 

well as in spontaneously increasing concentration of organic solvent (ethanol). The amino 

acids were classified as hydrophobic, intermediate and hydrophilic based on the free energy 

change by the transfer of amino acids from ethanol to water. Based on the data, a 

hydrophobicity scale was proposed for amino acid residues, where their free energy transfer 

values become more and more positive as the hydrophobic character of the protein increases 

(102). This revealed that hydrophobic amino acids will tend towards organic phase while 

hydrophilic amino acids will prefer to stay in the water. In other words, charged residues of 

the protein will tend towards the surface and in contrast, the hydrophobic residues will stay in 

39

Chapter 3 

the interior of the protein (25). The average hydrophobicity parameter was reported in 

literature for its relation to protein structure (66). However, this parameter was rarely 

investigated in context of the HIC. Average flexibility is another property which can be 

calculated by using Bhaskaran and Ponnuswamy’s scale. Average flexibility represents the 

average fluctuations of the all residues in a given protein. It was reported that proteins with 

high flexibility will have more chances to unfold and expose hydrophobic residues on the 

protein surface. This will increase hydrophobic interactions between proteins and adsorbents 

and result in longer retention of proteins during chromatographic experiments (33). 

Table 4: The physicochemical properties of the proteins investigated in this study 

Protein properties Scales Bioinformatic tools Source 

Average 

hydrophobicity 

Cowan-Whittaker’s 

scale, 

Expasy ProtScale tool Primary 

structure 

Miyazawa-Jernigan’s 

scale 

Tanford’s scale 

Average flexibility Bhaskaran and 

Ponnuswamy’s scale 


structure 

Average polarity Grantham’s scale, 

Zimmerman’s scale. 


structure 

Average bulkiness Zimmerman’s scale Expasy ProtScale tool Primary 

structure 

Average surface 

hydrophobicity 

Cowan-Whittaker’s 

scale 

Expasy ProtScale tool, 

MATLAB and STRIDE 

softwares 

Three 

dimensional 

structure 

The polarity scales proposed by Grantham and Zimmerman were used to quantify the polarity 

based on the protein sequence (96, 97). All the above mentioned properties were based on the 

primary structure of a protein. However, ASH was the most remarkable property in the 

40

Chapter 3 

context of HIC based on the exposed amino acid residues of a three dimensional protein 

structure. The ASH of a protein has a direct proportionality to the exposed hydrophobic 

surface area on the protein surface. The three crystal structures identified in this work have 

been compared for their hydrophobic surface residues and elution position during 

chromatography (Figure 9). The crystal structures of the enzymes were obtained using 

chimera as a visualization tool (http://www.cgl.ucsf.edu/chimera). The protein such as Serine 

threonine protein kinase (A) has very less hydrophobic surface residues in comparison to the 

protein Ubiquitin carboxyl terminal hydrolase 4 (B). Due to this reason, the protein A was 

eluted earlier than protein B during chromatography. In the same way, the protein B has less 

hydrophobic surface residues as compared to the protein Phosphogycerate mutase 1 (C). Due 

to the less hydrophobic residues, the protein B was eluted earlier than protein C. There are 

several other important enzymes identified at different elution ranges and can be analyzed at 

the molecular level for their hydrophobic content and corresponding chromatographic 

behavior. These surface hydrophobic residues can be quantified in terms of ASH from the 

three dimensional structure of a protein utilizing MATLAB and STRIDE as computational 

tools. ASH has been widely exploited to predict the retention times of model proteins during 

HIC (103). However, all the above mentioned properties have been never studied in relation 

to the host derived proteins. Therefore, the above mentioned properties were investigated for 

their correlation with the chromatographic behavior and potential to differentiate among the 

ligands. This work was planned to provide the data from a real bioprocess for designing the 

purification model of the recombinant proteins utilizing an in silico approach. 

41

Chapter 3 

A 

B 

C 

Figure 9: The crystal structures of three proteins identified in this work. These proteins were 

eluted at different positions during chromatography depending on the surface hydrophobic 

residues. The red and blue colors represent the hydrophobic and hydrophilic surface residues, 

respectively. 

42

Chapter 3 

3.1.2.3. Towards an in silico approach for the downstream processing of bioproducts 

The in silico approach is actually the computational models or simulations based on the 

experimental data which can be used for making hypothesis and predictions about any 

biological process (83, 84). This approach has been widely used in the pharmaceutical 

industry for drugs discovery as discussed in chapter 1. However, the in silico approach was 

rarely evaluated in practical terms for the downstream processing of proteins. Several research 

groups had proposed models for the prediction of the chromatographic behavior of proteins 

onto hydrophobic adsorbents with varying degrees of success but none of these approaches 

have gained a universal acceptance (26, 87, 103). The first and major criticism was that all the 

schemes were based on model proteins instead of real biological crude extract. The second 

criticism was that how can one predict about the separation of any specific protein from 

biological mixtures based on the data of few homologous model proteins and without 

considering the chromatographic fractionation of the host cell proteome. The third criticism 

was that those models were based on the retention time and / or volume of the model proteins 

and these two parameters have no applicability in terms of real bioprocess. At any specific 

space of time (minute), several proteins are supposed to be eluted instead of a single protein 

during chromatographic fractionation of a cell proteome. Instead of retention time and / or 

volume, the salt concentration should be specified for the protein elution under specific 

chromatographic conditions. They never tried a model based on the chromatographic 

separation of the total cell proteome as the approach is experimentally demanding and 

laborious. This approach has been adopted in this work as presented in Figure 10, where 

several techniques were employed ranging from chromatographic fractionation of cell 

proteome to identification and analysis of the proteins available in each chromatographic 

fraction. The identified proteins were characterized by several protein properties and these 

properties were then further correlated to the chromatographic behavior. The protein 

properties were also investigated for their ability to differentiate among the adsorbents. Aside 

43

Chapter 3 

of the comparative analysis of the ligands as a main ambition, this work was planned to 

collect the experimental data based on the chromatographic separation of a cell proteome. The 

information obtained regarding proteins elution ranges on different adsorbents and the 

corresponding protein properties can be used for designing the purification models of the 

recombinant proteins by an in silico approach. This work has given a first draft of the 

experimental data to design models and predict the elution position of the recombinant 

proteins during HIC through computer simulations. 

Figure 10: Schematic representation of the methodology towards an in silico approach. 

44

Chapter 3 

3.1.3. Results and Discussion 

3.1.3.1. Chromatographic profiles of different ligands 

The S. cerevisiae PBIVU02-1 was chosen as an expression system, because it was the first 

organism to have its genome completely sequenced. Scouting chromatographic experiments 

were performed in order to evaluate the separation behavior of the S. cerevisiae cell proteome 

on different ligands (Ether, Butyl and Hexyl) employing Toyopearl® as a common base 

support. A decreasing gradient of ammonium sulfate was performed from 1.6 to 0 M and 

seven fractions were collected at specific salt concentrations. The salt concentrations of the 

fractions have been specified by numbers such as 1 (1.6 M), 2 (1.4 M), 3 (1 M), 4 (0.8 M), 5 

(0.6 M), 6 (0.2 M) and 7 (0 M). These salt concentrations at elution positions were defined for 

the first time in order to investigate the proteins available in each chromatographic fraction. 

The prediction models reported in the previous studies were based on the retention time and / 

or volume of the model proteins instead of the salt concentrations at elution stage (62). 

However, the retention time and / or volume have no applicability for the fractionation of a 

cell proteome. Several proteins are supposed to be eluted at a fraction of time (minute), so to 

assign a specific elution time to any protein from a biological mixture is impractical. 

Therefore, the fractions were collected on specific salt concentrations during chromatography. 

The chromatograms revealed no clear matching in the separation profiles of the three 

adsorbents for the same crude extract (Figure 11). A high resolution was observed for 

Toyopearl-Ether- than -Butyl and -Hexyl. The reason could be the smaller bead size of 

Toyopearl-Ether. Another reason could be that resolution decreases when the chain length is 

increasing i.e. Toyopearl-Hexyl- and -Butyl have higher chain lengths than -Ether (51). These 

results confirmed that bead size and chain length of the ligands have a significant role in 

resolution. Overall resolution of chromatographic separation was very low and the reason can 

be the high amount of sample which was applied in this micro preparative chromatography. 

45

Chapter 3 

Figure 11: Represent the chromatographic profiles obtained after the fractionation of the cell proteome by HIC using Toyopearl-Hexyl, Toyopearl- 

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 

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 

M) and 7 (0.0 M). Each fraction was collected by pooling in triplicate. (- Refers to 280 nm mAU, --- is Buffer B %). 

46

Chapter 3 

The applied protein load (40 mg) for chromatographic experiments was within the range of 

dynamic binding capacities of the adsorbents. A high amount of proteins was adsorbed on 

Toyopearl-Hexyl- and -Butyl which confirmed the high dynamic binding capacities of these 

adsorbents. Toyopearl-Ether has shown less binding affinity and most of the proteins were 

lost in flow through as non retained components. The results revealed the binding affinities of 

the adsorbents for a cell proteome. In case of Toyopearl-Ether, almost 80% of the total load 

was lost during washing as evidenced by the Bradford method. However, for Toyopearl-Butyl 

and -Hexyl almost 50% of the proteins were lost in the flow through. The remaining half of 

the proteins were retained in the column and eluted according to their hydrophobicity during 

chromatography. A high amount of proteins was lost during flow through which confirmed 

the less hydrophobic nature of the S. cerevisiae cell proteome (104). The results suggested the 

use of high salt concentrations in order to increase the adsorption affinity of the yeast cell 

proteome during HIC. 

3.1.3.2. Two dimensional gel electrophoresis and proteins identification 

The two dimensional gels were performed for all the chromatography fractions collected 

within a specific operational window. Several proteins were selected from each gel for 

identification purposes; however in a few cases the tiny and weakly stained proteins were 

identified. Four out of the total identified proteins were visible on the gels with naked eyes, 

however were not visible when photographed and it is sometimes the case with weakly 

stained proteins (Figures 12-14). There are several reports where weakly stained proteins 

were identified, but the spots were not visible on the 2-D gel pictures (105-111). In total, 

ninety six proteins were identified using MALDI-ToF-MS, which revealed the applicability of 

the in-gel digestion method optimized in this work specifically for the proteins fractionated by 

HIC (Tables 5-7). The identified proteins can further provide the contaminant profile of the 

yeast cell proteome and is an addition to the proteomics technology. In the case of Toyopearl- 

47

Chapter 3 

Ether, some 2-D gels were observed to have the restricted number of protein spots which 

could be used as an advantage to have the targeted product on the specific conditions. Overall, 

three dimensional separation of the yeast cell proteome was performed, based on 

hydrophobicity (HIC), and then followed by isoelectric point and molecular weight (2-D 

PAGE). The three dimensional characterization can be a suitable method when choosing an 

efficient host for the protein purification (112). The chromatographic methods can be used in 

the prefractionation steps for proteomics studies. Several chromatographies have been used 

previously to decrease the dynamic range of the cell proteomes on 2-D gels such as heparin 

and hydroxyapatite used for the Haemophilus influenza and Escherichia coli, respectively (76, 

109). However, it was an initial effort to fractionate the yeast cell proteome by several 

hydrophobic adsorbents followed by its proteomics analysis. 

48

Chapter 3 

Figure 12: Represent 2-D D gels performed for the samples fractionated by Toyopearl-Ether. 

Each gel represents the specific fractions collected at different salt ranges such as A (1.6 M), B 

(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 

were selected for identification. The encircled spot has been explained in section 3.1.3.2. 

49

Chapter 3 

Figure 13: Represent 2-D D gels performed for the samples fractionated by Toyopearl-Butyl. 

Each gel represents the fractions collected at different salt ranges such as A (1.6 M), B (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 were 

selected for identification. The encircled spot has been explained in section 3.1.3.2. 

50

Chapter 3 

Figure 14: Represent 2-D D gels performed for the samples fractionated by Toyopearl-Hexyl. 

Each gel represents the fractions collected at different salt ranges such as A (1.6 M), B (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 were 

selected for identification. The encircled spots have been explained in section 3.1.3.2. 

51

Chapter 3 

Table 5. List of the identified proteins fractionated by Toyopearl-Ether and their characterization with different properties 

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 

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 

A2-TE OYE2_YEAST 44 6.13 3RND 0.4234 0.390 0.524 0.606 0.465 0.274 0.612 0.638 

A3-TE Fox2p 96 8.96 3QRF 0.4289 0.363 0.484 0.582 0.507 0.318 0.588 0.637 

A4-TE YN92_YEAST 69 6.15 3OQ0 0.4319 0.365 0.491 0.585 0.503 0.310 0.591 0.671 

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 

B2-TE SYF1_YEAST 100 5.17 3GIX 0.4396 0.372 0.528 0.608 0.500 0.290 0.593 0.667 

B3-TE TPIS_YEAST 26 5.74 3TH6 0.4411 0.408 0.503 0.597 0.454 0.302 0.599 0.688 

B4-TE APM2_YEAST 69 6.41 1HES 0.4436 0.402 0.507 0.596 0.476 0.316 0.603 0.667 

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 

C2-TE Fur1p 23 5.84 2JBH 0.4458 0.385 0.512 0.620 0.476 0.280 0.598 0.667 

C3-TE CAT8_YEAST 160 9.13 3SNP 0.4505 0.389 0.514 0.612 0.472 0.289 0.608 0.636 

C4-TE BUR1_YEAST 74 9.55 3R22 0.4495 0.390 0.511 0.614 0.478 0.284 0.624 0.649 

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 

D2-TE FSF1_YEAST 35 9.84 3OJ2 0.4584 0.412 0.518 0.617 0.461 0.279 0.614 0.648 

D3-TE YO316_YEAST 7 11.66 2Y6V 0.4612 0.414 0.520 0.619 0.443 0.277 0.643 0.627 

D4-TE IMH1_YEAST 105 5.52 3TCX 0.4618 0.415 0.526 0.620 0.457 0.275 0.645 0.634 

D5-TE FAF1_YEAST 38 9.33 3R3M 0.4620 0.406 0.528 0.621 0.455 0.274 0.614 0.698 

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 

E2-TE CORO_YEAST 72 5.64 2AQ5 0.4660 0.417 0.531 0.622 0.448 0.264 0.636 0.629 

E3-TE SPC2_YEAST 20 9.22 3SBT 0.4684 0.426 0.539 0.619 0.435 0.285 0.628 0.684 

E4-TE MSA2_YEAST 39 10.08 3POJ 0.4731 0.424 0.526 0.629 0.468 0.260 0.634 0.629 

E5-TE BOP3_YEAST 43 9.72 3PLT 0.4791 0.426 0.539 0.632 0.439 0.259 0.651 0.615 

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 

F2-TE ATG2_YEAST 178 5.62 2DYT 0.4771 0.427 0.531 0.622 0.454 0.261 0.637 0.652 

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 

F4-TE Fsf1p 31 9.84 3L06 0.4936 0.438 0.579 0.643 0.414 0.255 0.635 0.649 

F5-TE SPS1_YEAST 55 7.55 3CZ8 0.4987 0.436 0.547 0.638 0.454 0.252 0.635 0.660 

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 

G2-TE SC61G_YEAST 8 9.48 2WW9 0.5119 0.445 0.543 0.620 0.445 0.248 0.642 0.670 

G3-TE PDE2_YEAST 60 6.14 1MCO 0.5187 0.449 0.547 0.622 0.421 0.242 0.643 0.675 

G4-TE NUP53_YEAST 52 9.53 3P3D 0.6023 0.448 0.572 0.645 0.415 0.262 0.644 0.613 

G5-TE COX1 Protein 90 9.67 3OMA 0.6336 0.439 0.543 0.604 0.435 0.270 0.647 0.655 

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 

PDB databank, H ; Average surface hydrophobicity calculated by Cowan-Whittaker’s scale, I, J and K ; Average hydrophobicity calculated by the scales 

of Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. L and M ; Average polarity calculated by the scales of Grantham and Zimmerman, 

respectively. N ; Average flexibility calculated by Bhaskaran and Ponnuswamy’s scale, O ; Average bulkiness calculated by Zimmerman’s scale. 

52

Chapter 3 

Table 6. List of the identified proteins fractionated by Toyopearl-Butyl and their characterization with different properties 


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 

A2-TB YP191_YEAST 41 5.55 3C9P 0.4087 0.415 0.440 0.603 0.475 0.301 0.596 0.649 

A3-TB SMA1_YEAST 28 9.85 3RDJ 0.4189 0.382 0.448 0.565 0.465 0.319 0.606 0.653 

A4-TB YSY6_YEAST 7 10.43 3NYB 0.4192 0.388 0.450 0.588 0.560 0.279 0.583 0.655 

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 

B2-TB PABP_YEAST 64 5.71 2X4T 0.4244 0.371 0.499 0.590 0.489 0.277 0.592 0.613 

B3-TB FMP46_YEAST 15 9.38 2QL8 0.4442 0.404 0.498 0.593 0.485 0.278 0.594 0.671 

Medium 

B4-TB IDHH_YEAST 47 9.24 3BLX 0.4339 0.414 0.500 0.582 0.483 0.306 0.601 0.650 

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 

C2-TB GFD1_YEAST 21 9.76 3LCN 0.4434 0.382 0.505 0.583 0.485 0.373 0.596 0.626 

C3-TB YDL052C 33 9.67 3R0S 0.4412 0.384 0.499 0.588 0.475 0.312 0.575 0.668 

C4-TB UBP4_YEAST 105 8.50 3IRT 0.4471 0.392 0.512 0.604 0.469 0.299 0.603 0.650 

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 

D2-TB LDB7_YEAST 19 9.08 2R10 0.4457 0.388 0.507 0.594 0.466 0.291 0.598 0.605 

D3-TB TFC4p 120 5.14 3MA5 0.4573 0.408 0.495 0.592 0.478 0.288 0.595 0.678 

D4-TB DRS1_YEAST 85 5.42 3RC3 0.4593 0.405 0.512 0.602 0.465 0.285 0.602 0.626 

D5-TB TFC1_YEAST 73 5.23 2JO4 0.4619 0.408 0.506 0.605 0.481 0.283 0.603 0.651 

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 

E2-TB TEX1_YEAST 47 8.88 3MXJ 0.4630 0.414 0.528 0.615 0.450 0.256 0.607 0.635 

E3-TB FAF1_YEAST 38 9.33 3R3M 0.4620 0.405 0.526 0.609 0.456 0.275 0.613 0.585 

E4-TB TDH3p 35 6.46 1Q32 0.4647 0.405 0.534 0.607 0.450 0.255 0.608 0.627 

E5-TB RPN2_YEAST 104 5.85 2X5N 0.4712 0.417 0.534 0.637 0.449 0.258 0.610 0.639 

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 

F2-TB DSD1_YEAST 47 7.53 3ANU 0.4751 0.426 0.534 0.625 0.432 0.242 0.613 0.651 

F3-TB PLSC_YEAST 33 9.63 1IUQ 0.4770 0.446 0.535 0.635 0.431 0.235 0.615 0.670 

F4-TB YMX6_YEAST 105 9.72 2JXN 0.4826 0.429 0.542 0.622 0.422 0.256 0.680 0.585 

F5-TB MSA2_YEAST 39 10.08 3POJ 0.4731 0.412 0.526 0.615 0.468 0.268 0.634 0.629 

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 

G2-TB YSW1_YEAST 70 6.69 3OQC 0.5018 0.437 0.547 0.625 0.417 0.255 0.629 0.648 

G3-TB IF4A_YEAST 44 5.02 2G9N 0.5082 0.433 0.532 0.618 0.447 0.228 0.680 0.659 

G4-TB SGM1_YEAST 81 4.81 2YB7 0.5210 0.448 0.572 0.633 0.412 0.248 0.639 0.638 

G5-TB IMP2_YEAST 19 10.01 1L0N 0.5233 0.429 0.576 0.628 0.416 0.220 0.669 0.676 





53

Chapter 3 

Table 7. List of the identified proteins fractionated by Toyopearl-Hexyl and their characterization with different properties 


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 

A2-TH MSS51_YEAST 50 8.84 315X 0.4013 0.327 0.465 0.604 0.551 0.280 0.573 0.662 

A3-TH Eno1_YEAST 46 6.16 3OTR 0.4095 0.375 0.472 0.555 0.547 0.387 0.588 0.614 

A4-TH Eno2_YEAST 46 5.67 2XSX 0.4130 0.376 0.476 0.558 0.542 0.372 0.579 0.613 

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 

B2-TH YAE1_YEAST 16 5.22 2XE7 0.4268 0.377 0.493 0.600 0.488 0.338 0.604 0.639 

B3-TH DBf2_YEAST 66 8.86 3OQ0 0.4319 0.378 0.498 0.589 0.485 0.299 0.603 0.642 

Medium 

B4-TH Pho85 cyclin 34 9.65 2PK9 0.4274 0.380 0.500 0.603 0.484 0.309 0.602 0.670 

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 

C2-TH XRN1_YEAST 175 7.04 3PIE 0.4463 0.397 0.510 0.598 0.470 0.300 0.608 0.652 

C3-TH MCX1_YEAST 57 7.59 3P9D 0.4471 0.403 0.516 0.608 0.470 0.308 0.619 0.675 

C4-TH DEBYG2_YEAST 35 6.46 3STH 0.4345 0.405 0.527 0.612 0.462 0.287 0.595 0.627 

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 

D2-TH YO316_YEAST 07 11.66 3PLU 0.4534 0.409 0.515 0.604 0.468 0.296 0.604 0.627 

D3-TH UB4_YEAST 105 8.50 3IRT 0.4471 0.410 0.509 0.593 0.479 0.290 0.603 0.650 

D4-TH YL345_YEAST 58 6.01 2DWO 0.4536 0.414 0.520 0.611 0.464 0.295 0.594 0.659 

D5-TH AI2M_YEAST 97 9.67 3IFJ 0.4567 0.418 0.526 0.612 0.463 0.289 0.594 0.657 

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 

E2-TH YE119_YEAST 14 8.49 1M94 0.4594 0.426 0.524 0.613 0.458 0.271 0.629 0.652 

E3-TH YJHO_YEAST 104 5.29 3SDJ 0.4612 0.427 0.526 0.609 0.450 0.277 0.632 0.667 

E4-TH SNZ2_YEAST 32 5.40 1DTY 0.4631 0.429 0.527 0.605 0.445 0.270 0.645 0.648 

E5-TH METE_YEAST 85 6.07 3L7R 0.4660 0.430 0.534 0.621 0.452 0.276 0.644 0.655 

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 

F2-TH YPD1_YEAST 19 4.58 1QSP 0.4697 0.432 0.530 0.612 0.450 0.270 0.618 0.656 

F3-TH BOP3_YEAST 43 9.72 3PLT 0.4791 0.425 0.539 0.618 0.425 0.259 0.651 0.696 

F4-TH SNF12_YEAST 63 5.47 2QLV 0.4711 0.434 0.542 0.618 0.447 0.284 0.646 0.644 

F5-TH DYR_YEAST 24 7.67 3Q1H 0.4764 0.438 0.536 0.621 0.440 0.289 0.647 0.662 

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 

G2-TH PNG1_YEAST 42 6.22 1PNG 0.4784 0.432 0.549 0.625 0.433 0.255 0.648 0.644 

G3-TH PMG1_YEAST 27 8.81 4PGM 0.4843 0.435 0.552 0.627 0.427 0.248 0.649 0.657 

G4-TH RL20A_YEAST 20 10.30 3O58 0.4896 0.437 0.554 0.630 0.419 0.243 0.649 0.676 

G5-TH MLP1_YEAST 21 5.14 2XMF 0.5159 0.442 0.553 0.634 0.418 0.237 0.650 0.647 





54

Chapter 3 

3.1.3.3. Influence of the ligands chemistries and average protein properties 

3.1.3.3.1. Average hydrophobicity (AH) 

Average hydrophobicity is actually the average of the hydrophobicities of the overall residues 

in the primary structure of a protein. Average hydrophobicity has been reported for its relation 

with the protein structure (66). However, average hydrophobicity has been rarely investigated 

in the context of HIC. There are different hydrophobicity scales proposed by several research 

groups but there is still a debate on which one scale is the best (32). Average hydrophobicity 

was calculated for the tabulated proteins on the scales proposed by Cowan-Whittaker, 

Miyazawa-Jernigan and Tanford (Tables 5-7) (25, 92, 95). A statistically significant linear 

correlation (r 2 = 0.82, p < 0.0001) was reported between the average hydrophobicity and 

protein retention behavior (Figures 15 A-C). This confirmed that protein with high average 

hydrophobicity will take longer time to elute during chromatography due to the high 

hydrophobic interactions between proteins and adsorbents. In contrast, the protein with less 

hydrophobic residues in its sequence will result in less hydrophobic interactions and 

ultimately the protein will elute at high salt concentrations in the beginning of the 

chromatography. It was reported before with model proteins, that in addition to a hydrophobic 

surface area, it is important to have a higher overall number of hydrophobic residues in the 

protein sequence for a strong binding on HIC adsorbents (62). The high abundance of 

hydrophilic amino acids in the contact surface area was also reported to have an inverse 

relationship with the protein retention (113). However, these results confirmed the role of 

average hydrophobicity in HIC with the process proteomics approach. The hydrophobicity 

value for the same protein on the scale of Miyazawa-Jernigan was differed with 1 and 2 digits 

to the scales of Cowan-Whittaker and Tanford, respectively. The reason for this could be the 

different direct and indirect approaches applied by researchers while determining the 

hydrophobicity indices for individual amino acids (25, 92, 95). The hydrophobicity indices of 

55

Chapter 3 

amino acids by Cowan-Whittaker’s scale were derived by a direct chromatographic method 

and due to this it was adapted for calculating ASH. The average hydrophobicity has given a 

direct relationship with the retention of protein on the mentioned scales. This confirmed the 

role of average hydrophobicity in the retention mechanism of HIC. The global averages of the 

r 2 values were 0.85, 0.82 and 0.79 for the scales of Miyazawa-Jernigan, Cowan-Whittaker and 

Tanford, respectively. The scales of the Cowan-Whittaker and Miyazawa-Jernigan have 

revealed high correlation coefficient values than Tanford. It was also reported before that the 

scales of Cowan-Whittaker and Miyazawa-Jernigan have provided the best correlation for the 

prediction purposes in comparison to other scales (114). 

Figure 15 A: Represents the average hydrophobicity calculated by the scale of Cowan- 

Whittaker. The protein average hydrophobicity values of the three ligands were correlated with 

the respective salt concentrations where the fractions were collected. The error bars represent 

the standard deviations of the average hydrophobicity values in each fraction. The statistical 

analysis revealed a significant correlation (r 2 = 0.82; p < 0.0001). 

56

Chapter 3 

Figure 15 B: Represents the average hydrophobicity calculated by the scale of Miyazawa- 

Jernigan. The protein average hydrophobicity values of the three ligands were correlated with 

the respective salt concentrations where the fractions were collected. The error bars represent 

the standard deviations of the average hydrophobicity values in each fraction. The statistical 


57

Chapter 3 

Figure 15 C: Represents the average hydrophobicity calculated by the scale of Tanford. The 

protein average hydrophobicity values of the three ligands were correlated with the respective 

salt concentrations where the fractions were collected. The error bars represent the standard 

deviations of the average hydrophobicity values in each fraction. The statistical analysis 

revealed a significant correlation (r 2 = 0.79; p < 0.0001). 

Some proteins were observed to have similar values of average hydrophobicity but differed 

with the average surface hydrophobicity (ASH). In this case, the protein with high ASH was 

retained longer than the one with less ASH, although they have the same average 

hydrophobicity. This revealed that ASH has a more decisive role in HIC than average 

hydrophobicity. Although ASH is the most precise method, but the trends observed in case of 

average hydrophobicity cannot be ignored. The reason for this could be that, although amino 

acids on the surface are deciding for retention, the unfolding of proteins may occur during 

chromatography experiments resulting in the retention based on the total hydrophobicity of a 

58

Chapter 3 

protein. It has been reported before that the retention of proteins in reverse phase 

chromatography (RPC) was dependent on the total hydrophobicity of a protein (70). This can 

give an additional advantage to average hydrophobicity and this parameter should also be 

explored in RPC. The trend lines of the three ligands were statistically non significant which 

revealed that average hydrophobicity have no ability to differentiate among the hydrophobic 

characters of the three ligands (Figure 15 A-C). The reason could be that average 

hydrophobicity was representing the total residues of a protein instead of only surface 

hydrophobic residues. 

3.1.3.3.2. Average polarity (AP) 

The average polarity of the proteins has been reported in the previous studies for its 

contribution in the three dimensional structure of a protein; however average polarity has been 

rarely explored in HIC (64, 115-117). The average polarity was calculated for the tabulated 

proteins utilizing the scales proposed by Grantham and Zimmerman (Tables 5-7) (96, 97). 

The polarity values for early and late eluted proteins were more than 0.49 (49%) and less than 

0.41 (41%), respectively on Grantham’s scale. The same trend was also observed on 

Zimmerman’s scale where early eluted proteins had a higher polarity 0.35 (> 35%) than the 

late eluted ones 0.24 (< 24%). The membrane proteins were reported to have less polarity (< 

40%) while soluble proteins have higher polarity, approximately 47%. The membrane 

proteins with the less polarity need detergents to be separated from respective membranes due 

to hydrophobic interactions. (117). It was also reported before with model proteins that a 

decrease in the polar surface area of the protein will increase the binding affinity on the 

adsorbents (62). However, the effect of polarity was examined for the first time in this work 

with the process proteomics approach. The average polarity of the proteins has revealed a 

statistically significant (r 2 = 0.82, p < 0.0001) inverse correlation with the protein retention 

during HIC (Figure 16 A-B). 

59

Chapter 3 

Figure 16 A: Represents the average polarity calculated by Grantham’s scale. The protein 

average polarity values of the three ligands were correlated with the respective salt 

concentrations where the fractions were collected. The error bars represent the standard 

deviations of the average polarity values in each fraction. The statistical analysis revealed a 

significant correlation (r 2 = 0.89; p < 0.0001). 

60

Chapter 3 

Figure 16 B: Represents the average polarity calculated by Zimmerman’s scale. The protein 

average polarity values of the three ligands were correlated with the respective salt 




This revealed that proteins with high polar residues in the protein sequence were eluted at 

high salt concentrations at the beginning of the chromatography. In contrast, the proteins with 

less polar residues in the protein sequence were eluted at the end of the chromatography at 

low salt concentrations. The polar residues are usually the acidic and basic amino acids (Asp, 

Asn, Glu, Lys, Arg, Gln). These amino acid residues are highly polar and occur on the surface 

of protein at an aqueous interface. The non-polar residues such as (Ala, Val, Leu, Ile, Cys, 

Met, Pro, Phe, and Trp) are usually found in the interior portion of the proteins (117). Based 

on the abundance of these residues in a protein sequence, the polarity value has been assigned. 

61

Chapter 3 

The polarity value for the same protein on Grantham’s scale was almost one digit higher than 

Zimmerman’s scale due to the different approaches adopted by them. The r 2 values of the 

polarity with the protein retention were 0.89 and 0.76 for the scales of Grantham and 

Zimmerman, respectively. An inverse relationship between polarity and retention volume 

proved that proteins with less polarity will have a more non polar surface area and will result 

in stronger binding to HIC surface. In contrast, proteins with high polarity will have more 

polar and less non polar surface area, resulting in less binding to hydrophobic adsorbents. In 

other words, polarity has an inverse relationship with protein hydrophobicity. An inverse 

relationship was also observed between polarity and flexibility; the proteins with high polarity 

were less flexible and eluted at high salt concentrations. Although polarity has no decisive 

role in retention but still it can be considered as a contributing parameter during HIC. The 

average polarity has shown a correlation with the retention behavior of protein, but it has no 

potential to differentiate among the ligands. The reason can be that this property was derived 

from the primary structure of a protein instead of the exposed surface residues in the three 

dimensional structure. HIC has more relevance with the surface residues of a protein and is 

rarely affected by the total residues. Instead, RPC is a chromatographic method based on the 

total hydrophobic residues of a protein. Due to this reason, the average polarity might have 

the potential to differentiate the hydrophobic characters of the reverse phase chromatographic 

adsorbents. 

3.1.3.3.3. Average flexibility (AF) 

The average flexibility was calculated for the tabulated proteins using the Bhaskaran and 

Ponnuswamy’s scale (Tables 5-7) (98). The statistical analysis revealed a direct significant 

correlation (r 2 = 0.80, p < 0.0001) of average flexibility with the retention behavior of the 

proteins during chromatography (Figure 17). The direct relationship of flexibility with 

protein retention could be related to the abundance of cystein residues in the primary structure 

62

Chapter 3 

of a protein forming disulfide bridges. The protein with many cystein residues / or disulfide 

bridges will be less flexible towards conformational changes. In contrast, the protein with 

high flexibility will be more flexible towards conformational changes and when it come in 

contact of HIC surface, it will unfold or expose the internal surface in order to increase 

hydrophobic interactions with the adsorbent, consequently resulting in longer retention during 

chromatography (33, 62). The role of average flexibility in chromatographic separation has 

been confirmed here for the first time with the process proteomics approach. The direct effect 

of flexibility on the retention behavior has also revealed the direct relationship of flexibility 

with proteins hydrophobicity. If a protein has high hydrophobic residues in its sequence, the 

higher will be the flexibility and compressibility of that protein, which is in agreement of the 

previous paper (119). The proteins eluted at high salt concentrations at the beginning of the 

chromatography have less flexible nature than those proteins eluted at the end of the 

chromatography. The less flexible proteins have compact structures and hydrophobic parts of 

the proteins exist in the inner core and resulted in less hydrophobic interactions and earlier 

elution during chromatography. In contrast, the highly flexible proteins will unfold during 

chromatography and a hydrophobic part will come to the surface which will ensue high 

hydrophobic interactions and resulted in later elution during chromatography. Some proteins 

such as C4-TH and E4-TB were eluted earlier than D1-TH and F1-TB, respectively although 

they have almost identical ASH. This could be due to the higher flexibilities of the latter 

proteins resulting in longer retention during chromatography. The same behavior was also 

observed with model proteins where ribonuclease S has shown higher retention on Butyl- 

Sepharose than other proteins, although they have identical surface hydrophobicity. It was 

claimed that the higher flexibility of ribonulcease S might be favoring longer retention (63). 

63

Chapter 3 

Figure 17: Represents the average flexibility calculated by Bhaskaran and Ponnuswamy’s scale. 

The protein average flexibility values of the three ligands were correlated with the respective 

salt concentrations where the fractions were collected. The error bars represent the standard 

deviations of the average flexibility values in each fraction. The statistical analysis revealed a 


The flexibility and stability of the proteins were also reported for their applications in nature, 

where the organisms stay alive at -50°C to over 100°C. All the organisms have the same 20 

amino acids; however it could be the combine contribution of the amino acid residues 

balancing the stability and flexibility of proteins and allowing the organism to stay on both 

temperature extremes (120). The average flexibility has shown no relation to the molecular 

weight and isoelectric point values of the proteins. In other words, the flexibility values of the 

tabulated proteins were independent of whether a protein is larger or smaller and acidic or 

basic in nature. The flexibility has revealed a direct relationship with the protein retention 

64

Chapter 3 

during chromatography; however it has no ability to differentiate among the ligands for their 

hydrophobic characters. 

3.1.3.3.4. Average Bulkiness (AB) 

The average bulkiness was calculated for the tabulated proteins employing the scale of 

Zimmerman (Tables 5-7). The average bulkiness has revealed no relationship (r 2 = 0.00; p = 

0.86) with the retention behavior of the proteins during chromatography (Figure 18). The 

average bulkiness values for the tabulated proteins were in a narrow range and the reason for 

this could be the size exclusion effect. Although bulkiness was expected to have an effect on 

chromatographic behavior due to its close relationship with the size of proteins, no effect was 

observed in the retention behavior. In the same way, the molecular weight values of the 

tabulated proteins have not revealed any correlation as they were representing the small 

fraction of the total proteins present on each 2-D gel. It was also researched by Zimmerman 

who proposed the scales for bulkiness and polarity, that there was no relationship between 

bulkiness and polarity values of the proteins according to his scales (96). In other words, if 

bulkiness has no correlation with polarity then it is supposed to have no relation with protein 

hydrophobicity. Our results confirmed that bulkiness has no role in the chromatographic 

separations during HIC. 

65

Chapter 3 

Figure 18: Represents the average bulkiness calculated by Zimmerman’s scale. The protein 

average bulkiness values of the three ligands were correlated with the respective salt 


deviations of the average bulkiness values in each fraction. The statistical analysis revealed a 

non significant correlation (r 2 = 0.00; p = 0.86). 

3.1.3.3.5. Average surface hydrophobicity (ASH) 

The procedure as described by J.C. Salgado et al was used to compute the ASH values for the 

tabulated proteins utilizing the Cowan-Whittaker’s hydrophobicity scale (Tables 5-7) (32, 92). 

According to this scale, the hydrophobicity values were assigned to each amino acid residue 

based on the amino acid retention time in RP-HPLC. A statistically significant (r 2 = 90; p 

value < 0.006) direct relationship was reported between ASH and retention behavior on the 

respective adsorbents (Figure 19). The proteins with less ASH have less hydrophobic surface 

residues and hence eluted at the beginning of the chromatography in comparison to the 

66

Chapter 3 

proteins with medium or high ASH values. The proteins were classified into three groups such 

as low, medium or highly hydrophobic, depending on the ASH and the corresponding salt 

concentrations on which the proteins were eluted during chromatography. The proteins eluted 

at high salt concentrations in the beginning of the chromatography were grouped as less 

hydrophobic than those proteins eluted at the middle and / or at the end of the 

chromatography. 

Figure 19: Represents the average surface hydrophobicity calculated by Cowan-Whittaker’s 

scale. The protein average surface hydrophobicity values of the three ligands were correlated 

with the respective salt concentrations where the fractions were collected. The error bars 

represent the standard deviations of the average surface hydrophobicity values in each 

fraction. The statistical analysis on average revealed a significant correlation (r 2 = 0.90; p < 

0.006). 

The ASH values were 0.413, 0.458 and 0.522 for the proteins eluted at the beginning, middle 

and at the end of the chromatography, respectively. These ranges were in agreement of the 

67

Chapter 3 

published papers with model proteins (32, 62). This revealed that a protein with less ASH 

value retained for a shorter space of time than the protein with high ASH value. This kind of 

behavior was also reported by several research groups that binding affinity increased with the 

exposed hydrophobic residues of a protein. These results confirmed that hydrophobicity of the 

adsorbents and proteins have the combine contribution to increase the entropy of the system 

and resulted in hydrophobic interactions (118). The correlation coefficients (r 2 ) values of ASH 

with the respective salt concentrations were 0.95, 0.94 and 0.80 for Toyopearl-Hexyl-, -Butyl 

and -Ether, respectively. The r 2 values in the case of Toyopearl-Ether- was very less than - 

Butyl and -Hexyl. The reason can be the elution of the highly hydrophobic proteins at the end 

of the chromatography in the case of Toyopearl-Ether which exerted an effect on the overall 

trend line. Some proteins were reported to have high or similar ASH values than others but 

retained less i.e. C3-TH and E4-TB were eluted earlier than D3-TH and F1-TB, respectively. 

This revealed that higher the molecular mass of the protein, the longer it will stay on the 

adsorbent. The behavior based on protein molecular mass was not reported for Toyopearl- 

Ether due to its less hydrophobic nature which resulted in less ability to entrap larger proteins. 

Although ASH has been reported in the prediction of retention time of model proteins, 

however this parameter has been never studied in HIC as a function of the cell proteome (26, 

87). The salt and hydrophobicity ranges were defined for the first time in this work and can be 

used for the separation of target proteins employing HIC. ASH has revealed a direct 

relationship with the retention of proteins; however it has a limited ability to differentiate 

among different ligands. 

3.1.3.4. Comparative studies of the hydrophobic ligands based on average protein 

properties 

There are several reports where various ligands have been compared for their hydrophobicity 

utilizing the retention time of model proteins (22, 33, 61, 62, 99). However, the 

hydrophobicity of the ligands has been never explored with a cell proteome. The 

68

Chapter 3 

hydrophobicity of the ligands was investigated in this work with the yeast cell proteome. 

However, the comparison of ligands has not fully differentiated with the proteome wide 

approach (Figure 19). The reason could be the involvement of several protein properties 

affecting the retention behavior in HIC. Several protein properties have been demonstrated for 

their correlation with protein retention and their ability to differentiate among the 

hydrophobic ligands. The protein properties such as average surface hydrophobicity, average 

hydrophobicity, average flexibility and average polarity have shown a correlation with the 

chromatographic separation of the proteins. However, these properties may have less ability 

to differentiate among the ligands (Table 8). 

Table 8: Protein properties and their ability to differentiate among ligands and correlation with 

chromatographic behavior during HIC 

Protein properties 

Differentiation Correlation 

Average hydrophobicity × + 

Average polarity × - 

Average flexibility × + 

Average bulkiness × × 

Average surface hydrophobicity Trend + 

+; indicates positive correlation, - ; indicates negative correlation; ×; indicates no correlation 

The results revealed that ligands cannot be fully differentiated based on protein properties and 

can only be estimated based on retention time of the model proteins. The reason could be that 

several proteins are supposed to be eluted at specific retention time during chromatographic 

separation of a cell proteome as a function of ligand chemistry. Another observation was that 

69

Chapter 3 

ligands revealed less difference at high salt concentrations than at low salt concentrations. In 

other words, the differences among the ligands were less at high hydrophobic conditions than 

at low hydrophobic conditions. This kind of behavior was also reported in our group with 

model proteins, where the differences among the adsorbents were higher at low salt 

concentrations than at the high salt concentrations (121). The reason for this could be that at 

the extreme hydrophobic conditions, the hydrophobic potential of the mobile phase has 

covered the potential of the ligands to demonstrate according to their hydrophobicities. At low 

salt concentrations, the mobile phase has low hydrophobicity and the adsorption was more 

dependent on the hydrophobicity of the ligands. 

3.1.3.5. Application note 

Several important proteins have been separated through chromatographic and electrophoretic 

separations and identified through MALDI-ToF-MS. This work can help those interested in 

the purification of proteins listed in Tables 5-7, with the methodology as described in chapter 

2. It will simplify the work of researchers and they will not have to scout for chromatographic 

experiments to figure out the elution position of any tabulated protein. Any of the target 

protein can be purified by isocratic chromatography, without going through trial and error 

chromatographies. Some of the important enzymes were Isocitrate dehydrogenase (eluted at 

1.4 M salt gradient on Toyopearl-Butyl), Phosphoglycerate mutase 1 (eluted at 0 M on 

Toyopearl-Hexyl), Triosephosphate isomerase (1.4 M on Toyopearl-Ether), Methyl 

transferase (0.6 M on Toyopearl-Hexyl), Enolase 1 and 2 (1.6 M on Toyopearl-Hexyl), D- 

serine dehydratase (0.2 M on Toyopearl-Butyl), Exoribonuclease 1 and Glyceraldehyde 3 

phosphate dehydrogenase (1.0 M on Toyopearl-Hexyl). 

70

Chapter 3 

3.1.4. Partial conclusions 

• The hydrophobicity of the three ligands has been investigated for the first time with 

the proteome wide approach utilizing different protein properties. 

• The ligands were not fully differentiated with the proteome wide approach, but a trend 

was observed. 

• Several protein properties such as average hydrophobicity, average flexibility, average 

polarity, average bulkiness and average surface hydrophobicity were investigated for 

their correlation with the protein retention during chromatography. Average polarity 

has shown an inverse relationship to the retention behavior of the proteins during HIC. 

Average surface hydrophobicity, average hydrophobicity and average flexibility have 

revealed direct relationship with the protein retention during chromatography. 

• Among all the properties, ASH was the only property which has a decisive role in HIC. 

• The large scale identification and characterization of the proteins has explored the 

contaminant profile of the yeast cell proteome and is an addition to the proteomics 

technology. 

• The data obtained by exploring the chromatographic behavior of the yeast cell 

proteome on the three different ligands can be used to design the purification models 

of recombinant proteins utilizing the in silico approach. 

71

Chapter 3 

3.2. Influence of the different base support chemistries during 

hydrophobic interaction chromatography 


The comparison of the different base support chemistries as a function of the yeast cell 

proteome was explored for the first time in this work. This work was planned in the 

continuation of a previous report from our group where several ion exchange beads were used 

to investigate the separation behavior of the insect cell proteome instead of model proteins 

(19). Although several conclusions were made in that work, but still the approach was not 

complete in its essence and several techniques were missing. That approach was further 

progressed by exploring the different base supports in HIC employing some additional 

technologies. In this study, the Saccharomyces cerevisiae was selected as a host cell proteome 

due to the reasons such as its availability in protein data bank and it has the advantage of 

providing post translational modifications. An experimentally demanding and laborious 

approach was adopted ranging from chromatographic and electrophoretic separation of the 

cell proteome to identification and analysis of the proteins in each chromatographic fraction 

by mass spectrometry and bioinformatics tools. Moreover, certain protein properties were 

examined for their correlation to the chromatographic separation during HIC. These 

properties were also investigated for their ability to differentiate among the different base 

supports. In addition to the role of base supports chemistries, this work was planned to 

produce the data with the process proteomics approach which could be used to design the 

purification models for recombinant proteins utilizing the in silico approach. 

72

Chapter 3 

3.2.2. Chemistry of the base supports 

The adsorbents such as Toyopearl-Phenyl, Sepharose-Phenyl and Source-Phenyl were used 

with different base supports harbored phenyl as a common ligand. The physical properties of 

these adsorbents have been given in Table 9 as provided by the Vendor Company (99). The 

commercial adsorbents are usually composed of ligands and base supports of various nature. 

There are several base supports usually utilized in HIC such as Sepaharose ® , Toyopearl ® and 

Source ® 

that are composed of certain polymers in backbone chemistries i.e agarose, 

methacrylate and polystyrene. These polymers are usually of hydrophobic nature but were 

modified to the hydrophilic nature by coupling to certain ether and hydroxyl groups (99). The 

same was the case with polystyrene based adsorbents, however its chemistry has not reported 

yet. The chemistry of the three mentioned adsorbents has been shown in Table 10. 

Table 9: Physical properties of the adsorbents with different base support (99) 

Adsorbents Base supports Ligands Bead size 

(µm) 

Pore size 

(nm) 

a DBC 

(mg/mL) 

Toyopearl- 

Phenyl 

Sepharose- 

Phenyl 

Toyopearl ® Phenyl 100 100 40 

Sepharose ® Phenyl 34 65 35 

Source-Phenyl Source ® Phenyl 15 34 25 

a DBC; Dynamic binding capacity 

73

Chapter 3 

Table 10: The chemistry of base supports and ligands of the three adsorbents 

Base supports Ligand Adsorbents 

Polymethacrylate (Toyopearl®) 

Phenyl Ligand 


H CH 3 2 

C C 

O C 

O 

(HO) R 

H O 2 

C 

O 

C 

H 2 

C 

C 

CH 3 

H 2 

C 

C 

C O 

O 

R (OH) 

O 

C O 

C 

CH 3 

O 

HW- 

65 

O 

Agarose (Sepharose®) 

Phenyl Ligand 

Sepharose-Phenyl 

CH 2 OH 

OH O 

H 

H 

H 

O OH 

H 

O 

H 

O 

H OH 

O 

H 

CH 2 

H 

H 

O 

PS 

O 

H 2 

C 

OH 

C 

H 

H 2 

C 

O 

Polystyrene divinyl benzene (Source®) 

Phenyl Ligand 

Source-Phenyl 

O 

Not reported yet 

74

Chapter 3 


3.2.3.1. Chromatographic fractionation 

Scouting experiments were performed to explore the most suitable operational conditions for 

the chromatographic separations of the yeast cell proteome. These standardized 

chromatographic conditions are summarized in Table 11. A decreasing gradient of 

ammonium sulfate was performed from 1.6 to 0 M and seven fractions were collected at 

specific salt concentrations in order to investigate the proteins available in each 

chromatographic fraction. The chromatograms in Figure 20 depict the resolution behavior of 

the three base supports for the same crude extract. There was no clear matching in the 

separation profiles of the three adsorbents due to the differences in their base support 

chemistries. The peaks with the number of 6, 4 and 1 correspond to Sepharose-Phenyl, 

Source-Phenyl and Toyopearl-Phenyl, respectively. A high resolution was observed in case of 

Sepharose-Phenyl and Source-Phenyl, due to their smaller bead size than Toyopearl-Phenyl. 

One large peak was observed for Toyopearl-Phenyl, the reason could be the larger bead size 

of the base support, which resulted in a low resolution during chromatographic separation. 

The overall resolution of the three chromatograms was not high. The reason could be the high 

amount of the sample load applied during chromatographic fractionation. In HIC, it is the 

initial effort to report the selectivity of phenyl media for a crude extract. The experimental 

approach applied in this work, where fractionation of a soluble cell proteome was done 

instead of model proteins will pave the way for a direct comparison between adsorbents in 

terms of hydrophobicity towards proteins derived from a complex biological mixture. 

Information of this kind of work has been rarely available in literature where they utilized a 

host cell proteome for chromatographic fractionation instead of the model proteins (63). 

A high amount of proteins (almost 50%) was lost in the flow through and the remaining 

proteins were retained in the column. This kind of behavior was also reported in Section 3.1, 

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Chapter 3 

where approximately 50% of proteins were washed out during flow through. This revealed the 

high abundance of less hydrophobic proteins in the yeast cell proteome in comparison to the 

highly hydrophobic proteins. This also confirmed the less hydrophobic nature of the yeast cell 

proteome (104). 

Table 11: Standardized chromatographic conditions 

Feature 

Description 

Column volume 

5 mL 

Flow rate 

1 mL / min 

Equilibration buffer 10 CV a , 1.6 M, (NH 4 ) 2 SO 4 pH 7.5 

Sample load 

5 CV a , (8 mg / mL) 

Linear Gradient 

25 CV a 

Fraction volume 

3 mL 

CV a Column volume 

76

Chapter 3 

Figure 20: Represent the chromatographic profiles obtained after the proteome fractionation by HIC using Sepharose-Phenyl, Toyopearl-Phenyl and 

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. 

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 

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). 

77

Chapter 3 

3.2.3.2. The electrophoretic separation and proteins identification 

The chromatographic fractions were further explored for their protein content by 2-D PAGE. 

The protein concentrations of the chromatographic fractions were kept similar during their 

electrophoretic separation by 2-D PAGE. This would give an insight to the total mass and the 

number of protein contaminants recovered in each chromatographic fraction (19). The 2-D 

gels were important for mapping the chromatographic separation of the yeast cell proteome 

and to excise certain spots for protein identifications (Figures 21-23). The seventy seven 

proteins were identified by MALDI-ToF-MS from all the gels representing specific 

chromatographic fractions (Tables 12-14). The four proteins were visible on the gels with the 

naked eyes, however were not visible when photographed and it is sometimes the case with 

the weakly stained proteins (105-111). The proteins identified from each chromatographic 

fraction were collected at different salt concentrations and can be separated through isocratic 

chromatography. This will avoid trial and error chromatographies to figure out the elution 

position of any of the tabulated protein. The large scale identification and characterization of 

the proteins has explored the contaminant profile of the yeast cell proteome and is an addition 

to the proteomics technology. The identified proteins and the corresponding 2-D gels can also 

be used in future to identify proteins by relying on 2-D gel electrophoresis image databases 

(123). The previous work on HIC was limited to a few homologous model proteins and 

average surface hydrophobicity parameter, which may not portray a global picture of the 

retention mechanism during HIC (23, 62). However, the global analysis of the base supports 

and protein properties as a function of complex cell proteome has overwhelmed the 

shortcomings in the previous approaches. 

78

Chapter 3 

Figure 21: Represent 2-D D gels performed for the samples collected by Toyopearl-Phenyl. Each 

gel represents the fractions collected at different salt concentrations such as A (1.6 M), B (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 arrows were 

selected for identifications. 

79

Chapter 3 

Figure 22: Represent 2-D D gels performed for the samples fractionated by Sepharose-Phenyl. 

Each gel represents the fractions collected at different salt concentrations such as A (1.6 M), B 

(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 

selected for identifications. . The encircled spots have been explained in section 3.2.3.2. 

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Chapter 3 

Figure 23: Represent 2-D D gels performed for the samples fractionated by Source-Phenyl. Each 

gel represents the fractions collected at different salt concentrations such as A (1.6 M), B (1.4 

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 

selected for identifications. . The encircled spots have been explained in section 3.2.3.2. 

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Chapter 3 

Table 12. List of the identified proteins fractionated by Toyopearl-Phenyl and their characterization with different properties 


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 

Medium 

A2-TP SLU7P 41 8.57 2HBP 0.3901 0.319 0.419 0.604 0.464 0.365 0.591 0.613 

A3-TP YDR532cp 44 4.80 1RW7 0.4015 0.375 0.488 0.605 0.496 0.326 0.611 0.668 

A4-TP ENO2_YEAST 46 5.67 2XHO 0.4198 0.401 0.467 0.608 0.494 0.298 0.579 0.613 

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 

B2-TP YKL215Cp 49 8.11 1K1D 0.4375 0.403 0.507 0.611 0.526 0.293 0.587 0.597 

B3-TP PMG1_YEAST 27 8.81 3R7A 0.4427 0.385 0.520 0.608 0.467 0.297 0.608 0.644 

B4-TP DENR_YEAST 22 8.78 2WPV 0.4449 0.394 0.523 0.609 0.473 0.295 0.615 0.644 

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 

C2-TP SPC2_YEAST 20 9.22 3PSI 0.4445 0.415 0.539 0.612 0.485 0.285 0.573 0.684 

C3-TP SEC4_YEAST 23 6.62 1G16 0.4471 0.411 0.522 0.607 0.465 0.286 0.604 0.624 

C4-TP PCL2_YEAST 35 9.72 2PMI 0.4478 0.400 0.523 0.613 0.464 0.288 0.616 0.668 

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 

D2-TP PGM2_YEAST 63 6.18 3OLP 0.4624 0.404 0.532 0.621 0.453 0.264 0.612 0.625 

D3-TP YKL084W 13 6.42 3QWP 0.4672 0.418 0.535 0.618 0.456 0.280 0.625 0.659 

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 

E2-TP LOC1_YEAST 23 10.31 3O2Z 0.4842 0.429 0.542 0.624 0.464 0.252 0.629 0.634 

E3-TP DSD1_YEAST 47 7.53 3GWQ 0.4857 0.426 0.547 0.625 0.462 0.242 0.644 0.611 

E4-TP YPT52_YEAST 26 4.49 3RSB 0.4713 0.428 0.565 0.630 0.453 0.265 0.628 0.621 

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 

F2-TP VATE_YEAST 26 5.33 2KZ9 0.4843 0.438 0.578 0.629 0.455 0.270 0.628 0.658 

F3-TP IDHH_YEAST 47 9.29 2RNG 0.4938 0.434 0.542 0.634 0.454 0.256 0.641 0.650 

F4-TP COQ3_YEAST 36 6.51 2YBO 0.5010 0.441 0.533 0.630 0.445 0.270 0.645 0.664 

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 

G2-TP AIM4_YEAST 14 10.31 3RLS 0.5120 0.445 0.612 0.638 0.442 0.253 0.679 0.613 

G3-TP SPS1_YEAST 55 7.55 3CM1 0.5340 0.451 0.585 0.656 0.444 0.242 0.674 0.660 

G4-TP RL16B_YEAST 22 10.54 3R3T 0.5488 0.489 0.619 0.638 0.432 0.241 0.681 0.649 





82

Chapter 3 

Table 13. List of the identified proteins fractionated by Sepharose-Phenyl and their characterization with different properties. 

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 

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 

Medium 

A2-PS ORF194 21 8.50 2X5T 0.4184 0.384 0.497 0.594 0.490 0.321 0.604 0.628 

A3-PS YJM789 93 6.05 3A15 0.4245 0.334 0.448 0.566 0.532 0.343 0.579 0.604 

A4-PS RPIA_YEAST 28 5.63 3KWM 0.4351 0.408 0.526 0.625 0.465 0.288 0.584 0.630 

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 

B2-PS NDUS6_YEAST 12 9.37 3RHD 0.4202 0.405 0.520 0.600 0.462 0.289 0.608 0.605 

B3-PS BIG1_YEAST 39 5.20 3LTL 0.4231 0.368 0.542 0.586 0.440 0.326 0.610 0.694 

B4-PS LEUC_YEAST 85 5.61 3Q68 0.4342 0.401 0.492 0.578 0.465 0.285 0.612 0.626 

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 

C2-PS PRS7_YEAST 51 5.32 3BPT 0.4351 0.383 0.496 0.594 0.487 0.324 0.612 0.623 

C3-PS MSC3_YEAST 80 9.14 2WGP 0.4522 0.401 0.518 0.579 0.517 0.280 0.612 0.601 

C4-PS RE114_YEAST 48 9.26 2PMI 0.4478 0.377 0.506 0.578 0.481 0.253 0.629 0.655 

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 

D2-PS CDC73_YEAST 44 7.72 3OIP 0.4499 0.415 0.509 0.599 0.476 0.284 0.620 0.641 

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 

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 

E2-PS KPYK1_YEAST 54 8.00 1A3X 0.4583 0.411 0.537 0.624 0.449 0.264 0.616 0.645 

E3-PS UB4_YEAST 105 8.44 2V6Z 0.4609 0.411 0.539 0.605 0.454 0.249 0.612 0.651 

E4-PS VATC_YEAST 44 6.22 1U7l 0.4615 0.415 0.534 0.621 0.451 0.240 0.623 0.667 

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 

F2-PS METE_YEAST 85 6.07 3EG3 0.4757 0.410 0.534 0.621 0.452 0.234 0.632 0.655 

F3-PS ADH1_YEAST 36 6.21 3MEQ 0.4643 0.426 0.560 0.633 0.426 0.227 0.639 0.604 

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 

G2-PS DPM1_YEAST 30 7.70 2GEJ 0.4997 0.452 0.572 0.635 0.411 0.245 0.640 0.666 

G3-PS 54S L22 34 9.98 3R3T 0.5488 0.449 0.660 0.655 0.412 0.298 0.639 0.635 

G4-PS MCM3_YEAST 107 5.26 3F9V 0.5853 0.455 0.628 0.603 0.410 0.214 0.624 0.628 





83

Chapter 3 

Table 14. List of the identified proteins fractionated by Source-Phenyl and their characterization with different properties 


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 

Medium 

A2-SP SLU7P 41 8.57 2HBP 0.3901 0.334 0.419 0.604 0.464 0.365 0.591 0.613 

A3-SP RS6_YEAST 26 10.44 2XZM 0.4068 0.366 0.477 0.552 0.498 0.373 0.583 0.637 

A4-SP RM32_YEAST 21 9.86 3BYI 0.4174 0.373 0.492 0.562 0.512 0.358 0.612 0.653 

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 

B2-SP LRG1_YEAST 116 8.54 3BYI 0.4174 0.361 0.490 0.611 0.443 0.269 0.585 0.664 

B3-SP TDXH_AERPE 28 6.32 3A5W 0.4175 0.374 0.484 0.580 0.493 0.314 0.587 0.665 

B4-SP VID22_YEAST 102 5.06 3IFW 0.4232 0.385 0.488 0.619 0.446 0.314 0.603 0.635 

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 

C2-SP VAM6_YEAST 97 6.61 1KMD 0.4255 0.386 0.524 0.564 0.452 0.302 0.589 0.674 

C3-SP SKG3_YEAST 114 8.37 3MP8 0.4324 0.392 0.498 0.605 0.486 0.305 0.631 0.616 

C4-SP Cha1p 22 9.70 3O05 0.4375 0.398 0.506 0.605 0.472 0.302 0.601 0.631 

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 

D2-SP PWP2_YEAST 103 4.94 2Y9D 0.4442 0.415 0.518 0.617 0.464 0.275 0.604 0.635 

D3-SP YH139_YEAST 11 9.75 2YBA 0.4500 0.408 0.537 0.611 0.446 0.280 0.590 0.658 

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 

E2-SP 1pk1p 11 6.96 2XAM 0.4537 0.410 0.545 0.623 0.443 0.304 0.634 0.697 

E3-SP MTC1_YEAST 53 4.50 3KOX 0.4576 0.415 0.572 0.619 0.418 0.269 0.657 0.651 

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 

F2-SP RDL2_YEAST 16 9.65 3L7S 0.4699 0.431 0.532 0.615 0.424 0.294 0.660 0.658 

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 

G2-SP VPS64_YEAST 67 6.97 3R42 0.4927 0.423 0.598 0.651 0.426 0.283 0.639 0.628 

G3-SP BRE2 58 5.78 2QIY 0.4797 0.453 0.633 0.648 0.427 0.226 0.634 0.631 

G4-SP SPP41 99 8.93 2XF5 0.5263 0.464 0.617 0.653 0.410 0.219 0.651 0.629 





84

Chapter 3 

3.2.3.3. Influence of the base support chemistries and average protein properties 

3.2.3.3.1. Average hydrophobicity (AH) 

Average hydrophobicity is the classical parameter based on the overall hydrophobic residues 

of a protein. It was reported before that in addition to hydrophobic surface residues, it is 

important to have a higher overall number of hydrophobic residues in the protein sequence for 

the stronger binding to the HIC adsorbents (62). However, the average hydrophobicity has 

been never investigated with the process proteomics approach. Hence, the three different 

hydrophobicity scales were used to calculate average hydrophobicity from the primary 

structures of the tabulated proteins (Tables 12-14). A statistically significant (r 2 =0.86, p < 

0.0001) linear relationship was reported between the average hydrophobicity and protein 

retention behavior on different base supports (Figure 24 A-C). In some cases, the proteins 

were reported to have similar average hydrophobicity values but differed with average surface 

hydrophobicity. This revealed that two proteins can have the same amount of total 

hydrophobic residues in the primary sequence but can differ with surface hydrophobic 

residues. The average hydrophobicity values for some proteins were similar; however their 

elution position was different from each other. In this case, the proteins with high ASH 

retained longer than the one with low ASH, although they have similar average 

hydrophobicity. The proteins such as B3-TP, and F1-TP retained longer during 

chromatography than A4-TP and E3-TP, respectively. These results confirmed that ASH has a 

decisive role in protein retention during chromatography. The overall values of average 

hydrophobicity revealed that the scale of Miyazawa-Jernigan was differed with 1 and 2 digits 

to the scales of Cowan-Whittaker and Tanford, respectively. The reason for this difference 

could be the various direct and indirect approaches, while determining the hydrophobicity 

indices for individual amino acids (25, 92, 95). The mentioned scales were calculated with 

quite different approaches and often correlated very poorly (124). The global averages of the 

85

Chapter 3 

r 2 values from the data of the three base supports were 0.90, 0.89 and 0.79 for the scales of 

Miyazawa-Jernigan, Cowan-Whittaker and Tanford, respectively. It was also reported before 

that the scales of Cowan-Whittaker and Miyazawa-Jernigan have provided the best correlation 

for the prediction purposes in comparison to other scales (114). Although ASH is the most 

precise method, but the trends observed in case of average hydrophobicity cannot be ignored. 

The reason for this could be that, although amino acids on the surface are deciding for 

retention, the unfolding of proteins may occur during chromatographic experiments resulting 

in retention based on the total hydrophobicity of a protein. 

Figure 24 A: Represents the average hydrophobicity calculated by the scale of Cowan- 

Whittaker. The protein average hydrophobicity values of the three base supports were 

correlated with the respective salt concentrations where the fractions were collected. The error 

bars represent the standard deviations of the average hydrophobicity values in each fraction. 

The statistical analysis revealed a significant correlation (r 2 = 0.89; p < 0.0001). 

86

Chapter 3 

Figure 24 B: Represents the average hydrophobicity calculated by the scale of Miyazawa- 

Jernigan. The protein average hydrophobicity values of the three base supports were 


bars represent the standard deviations of the average hydrophobicity values in each fraction. 

The statistical analysis revealed a significant correlation (r 2 = 0.90; p < 0.0001). 

87

Chapter 3 

Figure 24 C: Represents the average hydrophobicity calculated by the scale of Tanford. The 

protein average hydrophobicity values of the three base supports were correlated with the 

respective salt concentrations where the fractions were collected. The error bars represent the 

standard deviations of the average hydrophobicity values in each fraction. The statistical 


It was reported that the folded proteins have less non polar surface area in comparison to the 

unfolded ones (125). This confirmed that during unfolding, the hydrophobic residues of the 

proteins were exposed on the surface and resulted in longer retention during chromatography. 

It was also described in the mentioned report that there has been no relation to the non-polar 

surface area and the size of protein. In the same way, no relation was observed here between 

the size and hydrophobicity of the proteins. It has been reported that there were very few 

buried residues which have zero accessible surface area. Most of the protein interior was 

composed of amino acids have contact with the solvent (125). This also revealed that most of 

88

Chapter 3 

the amino acids in a protein had a contribution in the retention behavior and due to this reason, 

the correlation of average hydrophobicity with retention seem sensible. The average 

hydrophobicity has been also reported for its significance in reverse phase chromatography 

(RPC). The RPC is based on the overall hydrophobicity derived from the primary structure 

instead of only surface residues of a protein (126). This revealed that average hydrophobicity 

and average surface hydrophobicity have decisive roles in HIC and RPC, respectively. The 

analysis of the two methods of protein hydrophobicity confirmed that ASH was more relevant 

to HIC than average hydrophobcity in terms of chromatographic separations. The trend lines 

(Figure 24 A-C) revealed that although average hydrophobicity has a role in HIC, but it has 

no ability to differentiate among the hydrophobic characters of the three base supports. 

3.2.3.3.2. Average polarity (AP) 

The average polarity was for the first time investigated for its correlation with protein 

retention behavior in HIC as a function of the cell proteome. The average polarity was 

calculated for the proteins using the scales proposed by Grantham and Zimmerman (Tables 

12-14) (96, 97). A statistically significant (r 2 = 0.82, p < 0.0001) inverse relationship was 

observed between polarity and retention behavior of the proteins on different base supports. 

The proteins eluted at high salt concentrations were highly polar than the proteins eluted at 

low salt concentrations. In other words, the proteins eluted at the beginning of the 

chromatography were more polar than those proteins eluted at the end of the chromatography. 

The average polarity values of the Grantham’s scale were higher in early eluted proteins (> 

0.49) than late eluted proteins (< 0.41). The same trend was also observed on Zimmerman’s 

scale, where early eluted proteins had high average polarity (> 0.35) than late eluted proteins 

(< 0.24). The reason can be that proteins with high polar residues retained for a shorter space 

of time than those with low polar residues. In a previous study, it has been mentioned that 

membrane proteins had low polarity below 40% while soluble proteins had higher polarity 

89

Chapter 3 

approximately 47%. The membrane proteins with the low polarity need detergents to be 

separated from respective membranes due to hydrophobic interactions (117). However, in the 

same study the polarity has not been correlated with the protein retention behavior in HIC. 

The polarity values for the same protein on Grantham’s scale were almost one digit higher 

than Zimmerman’s scale. This could be due to the different approaches used by the two 

researchers while calculating the polarity indices for individual amino acids. It has been 

reported that polar residues are normally located in the outer part of the protein while nonpolar 

residues in the inner portion (117). The polarity of a protein has a direct relationship 

with the number of polar residues (Asp, Asn, Glu, Lys, Arg and Gln) in the protein sequence. 

An inverse relationship was reported between polarity and retention behavior of the proteins 

(Figure 25 A-B). This revealed that the proteins with less polarity have a more non-polar 

surface area and resulted in stronger binding to the HIC surface. In contrast, proteins with 

high polarity had more polar and less non-polar surface area, resulting in less binding to 

hydrophobic adsorbents and earlier elution during chromatography. This also ensured that 

polarity has an inverse relationship with protein hydrophobicity. 

90

Chapter 3 

Figure 25 A: Represents the average polarity calculated by Grantham’s scale. The protein 

average polarity values of the three base supports were correlated with the respective salt 




91

Chapter 3 

Figure 25 B: Represents the average polarity calculated by Zimmerman’s scale. The protein 

average polarity values of the three base supports were correlated with the respective salt 




The r 2 values of the polarity with the retention of the proteins were 0.84 and 0.80 for the 

scales of Grantham and Zimmerman, respectively. An indirect relationship was also observed 

between polarity and flexibility of the proteins. The proteins with high polarity were less 

flexible and eluted at the beginning of the chromatography. These results confirmed the effect 

of average polarity in the chromatographic separation of proteins with the process proteomics 

approach. The polarity of the surface amino acids can also be used as an alternative parameter 

to average surface hydrophobicity and can be named as average surface polarity (ASP). The 

ASP parameter can be calculated for the tabulated proteins in the same way as ASH was 

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Chapter 3 

calculated. The ASP parameter can be used for the prediction of protein retention time in HIC. 

The average polarity has revealed no differences among the base supports for their 

hydrophobicity. 

3.2.3.3.3. Average surface hydrophobicity (ASH) 

There is no universally accepted method to be considered for the measurement of protein 

hydrophobicity; however it can be estimated through several hydrophobicity scales proposed 

by different co-workers (25, 92, 95). The hydrophobicity scale proposed by Cowan-Whittaker 

(92) was used to calculate ASH. This scale was based on a direct method and was derived 

from the amino acid retention time in RP-HPLC. The approach of J.C. Salgado et al (32, 92) 

was used for calculating ASH utilizing the bioinformatics tools. This property was 

demonstrated by averaging the hydrophobic potentials of the surface amino acid residues 

present in the three dimensional structure of a protein. All the amino acids on the surface of a 

protein have contributed in the ASH; however the eight non polar hydrophobic amino acids 

such as alanine, leucine, isoleucine, tryptophan, methionine, phenylalanine and proline have 

the major contributions. The ASH values have been calculated for the tabulated proteins 

(Tables 12-14). The statistical analysis revealed a significant direct correlation (r 2 = 0.91, p < 

0.002) between ASH and protein retention behavior on the respective adsorbents (Figure 26). 

The proteins were classified into three groups depending on the ASH and the corresponding 

salt concentrations on which the proteins were eluted. The proteins eluted at high salt 

concentrations in the beginning of the chromatography have less ASH values and were 

considered as less hydrophobic than the proteins eluted at the middle and / or at the end of the 

chromatography. Those proteins which have many hydrophobic residues on their surfaces 

were retained longer and eluted at the end of the chromatography. The data obtained for the 

proteins eluted at low salt concentrations such as 0.6 M, 0.2 M and 0 M have high importance 

for industrial applications, where the use of low salts is the preferred choice in HIC (24). ASH 

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Chapter 3 

values have been reported on average 0.403, 0.449 and 0.519 for the proteins eluted at the 

beginning, middle and at the end of the chromatography, respectively. These hydrophobicity 

values were in agreement to those in the literature (32, 62). This revealed that proteins with 

less ASH would retain for a shorter space of time than the protein of high surface 

hydrophobicity. The surface hydrophobicity can also be related to the hydrophobic surface 

area of a protein and both have linear relations between them and the retention volume (127). 

Figure 26: Represents the average surface hydrophobicity calculated by Cowan-Whittaker’s 

scale. The protein average surface hydrophobicity values of the three base supports were 


bars represent the standard deviations of the average surface hydrophobicity values in each 

fraction. The statistical analysis on average revealed a significant correlation (r 2 = 0.91; p < 

0.002). 

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Chapter 3 

The correlation coefficients (r 2 ) of ASH with retention of proteins were 0.93, 0.83 and 0.96 

for Toyopearl-Phenyl, Sepharose-Phenyl and Source-Phenyl, respectively. The correlation 

coefficient reported in the Sepharose-Phenyl case was less than Toyopearl-Phenyl and 

Source-Phenyl; the reason could be the heterogeneous distribution of the hydrophobic 

hotspots on the base support. This kind of behavior was previously reported with 

chromatographic separation of model proteins by Sepharose-Phenyl, where proteins of similar 

ASH have exhibited different retention times (63). 

In the previous reports, model proteins have been used to correlate ASH with the retention 

behavior. This correlation was never examined as a function of the complex cell proteome (26, 

27). The correlation of ASH with retention behavior was explored for the first time in this 

work with the yeast cell proteome. The experimental data about salt concentrations and 

corresponding hydrophobicity ranges at different elution stages can be exploited to predict the 

separation of recombinant proteins employing yeast as a host cell proteome. The separation of 

recombinant proteins by HIC can also be predicted in other host cell proteomes, once its ASH 

is calculated. ASH was the only property which has revealed the trend lines to partially 

differentiate among the base supports. 

3.2.3.3.4. Other protein properties affecting the chromatographic behavior 

The average flexibility was calculated for the tabulated proteins using the scale proposed by 

Bhaskaran and Ponnuswamy (Tables 12-14) (98). The average flexibility has revealed a 

statistically significant (r 2 = 0.80; p < 0.0001) direct correlation with the protein retention 

during chromatography (Figure 27). The average flexibility has been reported before for its 

direct relationship with the retention time of model proteins in HIC (62). This could be due to 

the presence of cystein residues in a protein forming disulfide bridges. A protein with more 

cystein content / and or disulfide bridges will be highly stable and less flexible towards 

conformational changes. In contrast, proteins with less cystein residues will be highly flexible 

95

Chapter 3 

towards conformational changes and when it comes in contact of HIC surface, it will unfold 

or expose the internal surface in order to increase hydrophobic interactions and result in 

longer retention during chromatography (33, 62, 128). 

Figure 27: Represents the average flexibility calculated by Bhaskaran and Ponnuswamy’s 

scale. The protein average flexibility values of the three base supports were correlated with the 

respective salt concentrations where the fractions were collected. The error bars represent the 

standard deviations of the average flexibility values in each fraction. The statistical analysis 

revealed a significant correlation (r 2 = 0.80; p < 0.0001). 

It was also reported by Tanford that proteins with native conformations were completely 

inflexible and conformational entropy was zero. In contrast, the unfolded proteins have high 

flexibility towards conformational changes (25). This revealed that proteins with less 

flexibility had less conformational entropy and high stability. In this work, average flexibility 

was correlated with the retention of proteins derived from a host cell proteome. Some proteins 

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Chapter 3 

such as D3-SP, B4-PS and B4-SP were eluted earlier during chromatography than E1-SP, C1- 

PS and C1-SP, respectively although they have almost similar ASH. This could be due to the 

higher flexibilities of the proteins which allow them to retain longer during chromatography. 

The same behavior was also reported with model proteins where ribonuclease S has shown 

higher retention on Sepharose-Butyl than other proteins, although they had almost identical 

surface hydrophobicity. It was claimed that higher flexibility of ribonulcease S might be 

favoring longer retention (63). The highly flexible proteins have revealed more unfolding and 

most of the hydrophobic residues were exposed which has given tight binding and in result 

most of them eluted at the end of the chromatography. Less flexible proteins were not able to 

stay longer due to their compact nature and most of hydrophobic parts were in the inner core, 

which resulted in less hydrophobic interactions and elution occurred at the beginning of the 

chromatography. Although the flexibility has no decisive role in retention, however it can be 

considered as the contributing parameter during HIC. The flexibility has given a correlation 

with retention of proteins; however it has not exhibited any differences among the base 

supports for their hydrophobic characters. 

The average bulkiness was also calculated for the tabulated proteins using Zimmerman’s scale 

(Tables 12-14). The average bulkiness of the proteins has revealed no significant relationship 

(r 2 = 0.04, p = 0.38) with the chromatographic separation of proteins in HIC (Figure 28). The 

bulkiness has been never reported for its contribution in HIC. In the previous report, 

Zimmerman had investigated to correlate polarity and bulkiness of cytochrome c protein in 

different organisms (96). However, no significant trend was observed between polarity and 

bulkiness in his studies. This also confirmed that if bulkiness has no relation with the polarity 

of the proteins, then it is supposed to have no relation with protein hydrophobicity as 

evidenced here. The bulkiness has also exhibited no differences among the different base 

supports. 

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Chapter 3 

Figure 28: Represents the average bulkiness calculated by Zimmerman’s scale. The protein 

average bulkiness values of the three base supports were correlated with the respective salt 


deviations of the average bulkiness values in each fraction. The statistical analysis revealed a 

non significant correlation (r 2 = 0.04; p = 0.38). 

3.2.3.4. Comparative studies of the different base supports based on average protein 

properties 

The comparison of the base supports has been done for the first time in this work with the 

proteome wide approach. The protein properties such as average surface hydrophobicity, 

average hydrophobicity, average flexibility and average polarity have shown a correlation 

with the retention of proteins during chromatography. However, these properties have a 

limited ability to differentiate among the base supports for their hydrophobicity (Table 15). 

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Chapter 3 

Table 15: Average protein properties for their correlation with retention time and the ability to 

differentiate among the base support for their hydrophobicity. 

Protein properties 

Differentiation 

Correlation 

Average hydrophobicity × + 

Average polarity × 

_ 

Average flexibility × + 

Average bulkiness × × 

Average surface hydrophobicity Trend + 

+ indicates positive correlation, - indicates negative correlation; × indicates no correlation 

These results revealed that protein properties have a limited ability to differentiate among the 

base supports for their hydrophobicity. At any specific fraction of time, several proteins are 

supposed to be eluted during chromatographic separation of a cell proteome. Due to this 

reason, assigning a specific retention time to any protein is impractical. This revealed that the 

proteome wide approach has a limited ability to differentiate among the base supports. 

In two cases (1.6 M and 0 M), the Sepharose-Phenyl has given slightly high hydrophobic 

proteins than Toyopearl-Phenyl. This unexpected behavior of Sepharose-Phenyl was also 

reported before; where proteins with high ASH were eluted earlier than those with lower ASH. 

The reason could be the heterogeneous distribution of hydrophobic hotspots on the 

Sepharose-Phenyl, due to which an unusual behavior was reported in the retention mechanism 

(63). Another observation was that base supports revealed less differences at high salt 

concentrations than at low salt concentrations. In other words, the differences among the base 

supports were less at high hydrophobic conditions than at low hydrophobic conditions. This 

kind of behavior was also reported in our group with model proteins, where the differences 

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Chapter 3 

among the adsorbents were higher at low salt concentrations than at the high salt 

concentrations (121). The reason for this could be that at the extreme hydrophobic conditions, 

the hydrophobic potential of the mobile phase has covered the potential of the base supports 

to demonstrate according to their hydrophobicities. At low salt concentrations, the mobile 

phase has low hydrophobicity and the adsorption was more dependent on the hydrophobicity 

of the base supports. 

3.2.3.5. Application note 

This work can help those interested in the purification of proteins listed in Tables 12-14. It 

will simplify the work of researchers and they will not have to scout for chromatographic 

experiments to figure out the elution position of any tabulated protein. The protein can be 

purified by isocratic chromatography, without going through trial and error chromatographies. 

Some of the important enzymes are Pyruvate kinase (eluted at 0.6 M on Sepharose-Phenyl), 

Phosphoglycerate mutase 2 (eluted at 0.8 M on Toyopearl-Phenyl), Polymerase A (0 M on 

Source-Pheny), Methyl transferase (0.2 M on Sepharose-Phenyl), Thiosulfate sulphur 

transferase (0.2 M on Source-Phenyl), Enolase 2 (1.6 M on Toyopearl-Phenyl), D-serine 

dehydratase (0.6 M on Toyopearl-Phenyl) and Alcohol dehydrogenase (0.2 M on Sepharose- 

Phenyl). 

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Chapter 3 


• The influence of the base supports has been investigated for the first time with the 

proteome wide approach utilizing different protein properties. The results revealed 

that protein properties have limited ability to differentiate among the base supports. 

• The base supports revealed less difference at high salt concentrations than at low salt 

concentrations. In other words, the differences among the base supports were less at 

high hydrophobic conditions than at low hydrophobic conditions. The reason could be 

the effects of mobile phase hydrophobicity. 

• Several protein properties such as average hydrophobicity, average flexibility and 

average surface hydrophobicity have revealed direct relationships with the retention of 

proteins. Average polarity has given an inverse relationship to the retention of proteins 

during HIC. 

• Among all the properties, ASH was the only property which has a decisive role in the 

retention behavior of proteins during chromatography. 

• The data collected from the three base supports in this process proteomics approach 

can be used to design the purification models by the in silico approach. 

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Chapter 3 

3.3. Protein distributions on 2-D gels: the influence of molecular 

weight and isoelectric point coordinates 


The separation behavior of the yeast cell proteome by hydrophobic interaction 

chromatography (HIC) was examined utilizing two dimensional polyacrylamide gel 

electrophoresis (2-D PAGE). The yeast cell proteome was fractionated on different 

hydrophobic adsorbents and chromatographic fractions were collected on specific salt 

concentrations. The chromatographic fractions were further analyzed by 2-D PAGE to 

investigate the contaminant profile in each chromatographic fraction. This chapter will give 

an additional understanding to the influence of ligands and base supports on the 

chromatographic separation of the yeast cell proteome. As expected, Toyopearl-Hexyl and 

Toyopearl-Butyl have shown increased protein retention in comparison with Toyopearl-Ether. 

Moreover, an influence of protein size and isoelectric point (pI) was also revealed as a 

function of the adsorbent type. Larger and / or neutral proteins showed increased while acidic 

(pI < 6) and basic (pI > 8) proteins depicted decreased or increased retention, depending on 

the adsorbent type. 

In addition to the separation behavior of yeast cell proteome during chromatography, an 

overview of the yeast cell proteome was gained. A high amount and a higher number of the 

proteins on 2-D gels were reported at the beginning of the chromatography than at the end. 

This confirmed the less hydrophobic nature of the yeast cell proteome. A higher number of 

smaller and basic proteins were observed on 2-D gels in comparison to larger and acidic 

proteins. This further showed the nature of the yeast cell proteome. 

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Chapter 3 


3.3.2.1. Unfolding of proteins during HIC 

The 2-D gels were performed for all the chromatographic fractions collected through six 

different hydrophobic adsorbents and stained with colloidal blue method in order to get the 

precise number of spots available in each fraction. The 2-D gels have been presented in 

Figures 12-14 (Section 3.1) and Figures 21-23 (Section 3.2). Some denatured spots were 

observed for the fractions collected at high salt concentrations on Source-Phenyl and 

Toyopearl-Hexyl representing the conformational changes occurred during chromatography. 

However, Toyopearl-Butyl and Sepharose-Phenyl has revealed less conformational changes 

and solid spots were observed on the 2-D gels. The conformational changes at high salt 

concentrations during chromatography were also reported before with model proteins (122, 

129-131). The ratio of the conformational changes of proteins was high on the gels collected 

at the high salt concentrations and extreme hydrophobic adsorbents. This revealed that 

conformational changes during chromatography have dependence on the salt concentration 

and the adsorbent type. This behavior was also reported before that by increasing the 

ammonium sulfate concentration and the chain length of ligands, the unfolding of protein 

increases on the HIC surface. This is the reason that unnecessary high salt concentration and 

high chain lengths of the ligands should not be preferred (129). The conformational changes 

observed on 2-D gels further reinforced the correlations observed in Section 3.1 and Section 

3.2, between flexibility and the corresponding retention volume. Some protein loss was also 

observed while comparing the amount of sample applied during chromatography and proteins 

obtained in the fractions after chromatography. Small amount of proteins (almost 5 mg) was 

lost elsewhere on the hydrophobic surface. The reason could be the unfolding of the proteins 

which resulted in less recovery in the chromatography fractions. These results confirmed the 

protein unfolding during HIC with the proteome wide approach. 

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Chapter 3 

3.3.2.2. Protein distributions on 2-D gels 

Proteins derived from S. cerevisiae cell proteome have shown less binding affinity on the 

adsorbents based on hydrophobic interactions. A maximum number and a high amount (mg) 

of proteins were eluted at the beginning of the chromatography than at the end (Figure 29 A- 

B). These results confirmed the less hydrophobic nature of S. cerevisiae cell proteome. The 

hydrophobic proteins in the S. cerevisiae cell proteome were reported to be 16% of the total 

cell proteome. This was very less amount than reported for other cell proteomes (104). The 

less hydrophobic nature of the yeast cell proteome suggested high hydrophobic conditions for 

the efficient separation of the cell proteome during HIC. The analysis of 2-D gels has also 

revealed the variation in the binding affinity of the different ligands for the same proteome. In 

case of Toyopearl-Ether, a higher number of protein spots were eluted at the beginning of the 

chromatography than at the end. The reason could be the less hydrophobic nature of the 

Toyopearl-Ether. In contrast, Toyopearl-Hexyl has shown higher numbers of protein spots at 

the end of the chromatography than at the beginning because of its high hydrophobic nature. 

In the similar fashion, the 2-D gels analysis has also revealed the variation in the binding 

affinity of the base supports for the same proteome. In case of Toyopearl-Phenyl, a higher 

number of proteins were eluted in the beginning of the chromatography than at the end and 

the reason could be the less hydrophobic nature of the base support. Sepharose-Phenyl and 

Source-Phenyl have shown higher numbers of spots at the end of the chromatography. This 

revealed the high binding affinity of the two adsorbents for the yeast cell proteome. 

When the adsorption affinity of polymethacrylate based media was compared with phenyl 

ligated media, the adsorption affinity was higher in earlier than later one. Toyopearl-Phenyl 

with phenyl ligand has not given higher adsorption of proteins when compared to Toyopearl- 

Butyl and Toyopearl-Hexyl. This confirmed that aromatic interactions have no effect on the 

adsorption affinity of the adsorbents (122, 132). 

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Chapter 3 

Figure 29 A: Represent the total number of spots and amount of proteins (mg) in each fraction 

as a function of the different base supports. The number of protein spots and amount of 

proteins (mg) revealed a significant correlation with the respective salt concentrations where 

the fractions were collected. 

105

Chapter 3 

Figure 29 B: Represent the total number of spots and amount of proteins (mg) in each fraction 

as a function of the different ligands. The number of protein spots and amount of proteins (mg) 

revealed a significant correlation with the respective salt concentrations where the fractions 

were collected. 

3.3.2.3. The effect of protein molecular mass on the chromatographic behavior 

Other component in evaluating the experimental data obtained after 2-D gel electrophoresis is 

the potential relationship between protein molecular weight and elution behavior, as a 

function of elution position and adsorbent type. In a previous report, molecular mass of the 

protein has not shown any correlation with protein retention in ion exchange chromatography 

(19). However, the effect of molecular mass was reported in HIC using the retention time of 

the model proteins (33). A few model proteins were used for the analysis which may not give 

a complete picture of all the contributing parameters involved in protein retention. In this 

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Chapter 3 

work, the effects of larger proteins (> 50 KDa) on retention have been examined as a function 

of the adsorbent type. The number of larger proteins in each fraction was divided by the total 

number of proteins to normalize the data. The normalized values of the larger proteins from 

the three ligands was presented in Figure 30, where the larger proteins were found to be 

higher at low salt concentrations than at high salt concentrations. In other words, the larger 

proteins were higher in the latter fractions in comparison to the earlier ones. This revealed that 

larger proteins retained longer on the adsorbents. In case of larger proteins, the retention was 

higher for Toyopearl-Butyl and Toyopearl-Hexyl in comparison to the other adsorbents. It 

was reported before that if the pore space of the adsorbent is smaller than the dimensions of 

the magnitude as the protein has, then it will result in less retention of protein (33). However, 

this was not applicable on Toyopearl-Ether and Toyopearl-Phenyl adsorbents where larger 

proteins has shown less retention, although they have high pore size (100 nm). The reason 

could be the less hydrophobic nature of the two adsorbents which retained larger proteins for 

a shorter time during chromatography. In a similar way, Source-Phenyl has given less 

retention to larger proteins, although it has a high hydrophobic nature, but the pore size is 

smaller (34 nm). These results declared that the pore size and hydrophobicity of the 

adsorbents could be the combine contributing parameters affecting the retention of larger 

proteins. 

In case of smaller proteins it was reported once that it has no effect on retention (33). 

However, in another report it was reported that smaller proteins exhibited a decrease in 

retention due to the less contact surface area with adsorbents (68). This was confirmed in this 

work when smaller proteins have exhibited an inverse relationship to the corresponding 

retention behavior and most of the smaller proteins were eluted at the beginning of the 

chromatography. The reason could be that smaller proteins have less non-polar surface area to 

interact with the adsorbents. 

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Chapter 3 

When the number of smaller and larger proteins was compared, smaller proteins were three 

times higher than larger proteins on the 2-D gels. This revealed that S. cerevisiae cell 

proteome have a higher proportion of smaller proteins than larger proteins. 

Figure 30: Represents the normalized values of the larger proteins (Mr > 50) in each fraction as 

a function of the different ligands. The larger proteins revealed a significant correlation with 

the respective salt concentrations where the fractions were collected. 

3.3.2.3. The influence of protein isoelectric point on the chromatographic behavior 

The effect of mobile phase pH and isoelectric point of the proteins are the parameters least 

studied in HIC. At the start of HIC method, it was reported that there is no obvious 

relationship between retention and isoelectric point of individual proteins (68). However, in 

the follow up studies some clear pH effects were observed, where basic proteins has shown 

longer retention when the mobile phase pH was close to their pI and acidic proteins exhibited 

less retention with basic pH values. In other words, the protein retention was increased at 

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Chapter 3 

acidic pH and decreased at basic pH values of the mobile phase (43). To study the sole effect 

of the pI, a neutral pH was maintained during chromatographic experiments. The neutral pH 

is also a preferred choice in the downstream processing of proteins. The chromatographic 

fractions were analyzed on 2-D gels and the normalized values of the acidic and basic proteins 

were calculated. The normalized values of the basic proteins in each gel (fraction) were 

presented versus corresponding elution position for different ligands (Figure 31). A higher 

number of basic proteins were found at low salt concentrations at the end of the 

chromatography which revealed the longer retention of basic proteins during chromatography. 

The Sepharose-Phenyl and Toyopearl-Butyl have given longer retention to the basic proteins 

in comparison to the other adsorbents. This revealed the partial negative charges on these 

adsorbents. The negative charges on adsorbents may attract the positive charges on basic 

proteins and resulted in ionic interactions in addition to HIC. The combination of the ionic 

and hydrophobic interactions resulted in longer retention of the basic proteins during 

chromatography. The partial negative charges on hydrophobic adsorbents were also reported 

in literature, although the ration of negative charges varied among adsorbents (133). The 

uncharged adsorbents were reported to be preferred for the true hydrophobic interactions, 

otherwise at low salt concentrations; the interaction will be more electrostatic than 

hydrophobic, which can affect the separation based on hydrophobic interactions (43). 

However, in other reports the presence of charges on the adsorbents was favored in order to 

maintain the native structures of the proteins (21, 102). 

The neutral proteins pI (6-8) and acidic proteins (pI < 6) have been eluted in almost equal 

distribution during the chromatographic separations. The reason could be the neutral pH of 

the mobile phase, which allowed the neutral proteins to elute on the basis of hydrophobicity. 

The analysis of 2-D gels revealed that the gels at the end have mostly neutral proteins, while 

acidic and basic proteins are rarely available. The proteins with pI around 6, 7 and 8 have 

been highly retained at neutral pH and the reason could be that the charge was near to zero 

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Chapter 3 

and hydrophobic interactions were at the maximum extent between proteins and adsorbents 

(44). These results confirmed the effects of isoelectric point values of the proteins on the 

chromatographic separation as a function of adsorbent type. A high number of basic proteins 

have been reported on 2-D gels in comparison to acidic proteins. This revealed the high 

abundance of basic proteins in S. cerevisiae cell proteome. 

Figure 31: Represents the normalized values of the basic proteins (pI > 8) in each fraction as a 

function of different ligands. The basic proteins revealed a significant correlation with the 

respective salt concentrations where the fractions were collected. 

110

Chapter 3 


• A high amount and a higher number of protein spots were reported at the beginning of 

the chromatography than at the end. This confirmed the less hydrophobic nature of the 

S. cerevisiae cell proteome. 

• The denaturation of proteins was confirmed in HIC with the proteome wide approach. 

• Aromatic interactions were confirmed to have no effect on the retention behavior of 

proteins during HIC. 

• The effect of molecular weight of the proteins was studied in relation to the adsorbent 

type. The pore size and hydrophobicity of the adsorbents were reported as the main 

parameters responsible for the retention of the larger proteins. 

• The effect of the isoelectric point of proteins was studied in relation to adsorbent type. 

The neutral and acidic proteins have given equal distributions over 2-D gels. The 

partial negative charges were confirmed on the hydrophobic adsorbents which has 

given rise to ionic interactions in addition to hydrophobic interactions and resulted in 

longer retention of basic proteins during chromatography. 

• A higher number of acidic and smaller proteins were reported in the cell proteome in 

comparison to basic and larger proteins, which can further reflect the nature of S. 

cerevisiae cell proteome. 

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Chapter 4 

CONCLUSIONS

Chapter 4 

4. Conclusions and Remarks 

4.1. General conclusions and remarks 

This thesis explored for the first time the comparative analysis of hydrophobic ligands and 

base supports in hydrophobic interaction chromatography (HIC) as a function of complex cell 

proteome. Several protein properties were examined for their correlation with the protein 

retention behavior in HIC. The protein properties were also explored to determine their 

potential to differentiate among the ligands and base supports. The previous studies of HIC 

were based on the retention time of model proteins. Hence, a process proteomics approach 

was applied to explore the hidden underlying parameters in HIC. Although very laborious and 

experimentally demanding, the present approach has led to conclusions which are valid for a 

complex cell proteome and which refers to two families of hydrophobic adsorbents. This 

work has produced a first draft of the experimental data which can be used to predict the 

elution position of any target protein utilizing an in silico approach. 

4.2. Influence of different ligands 

• The hydrophobicity of the ligands was comparatively investigated in the previous 

reports exploiting the retention time of model proteins. However, the hydrophobicity 

of the ligands has been investigated for the first time with the yeast cell proteome 

utilizing several protein properties. The protein properties have not fully differentiated 

among the ligands. This confirmed that protein properties have limited ability to 

differentiate among the ligands for their hydrophobicity. 

4.3. Influence of different base supports 

• The influence of the base supports has been investigated for the first time with the 

proteome wide approach utilizing different protein properties. The results revealed that 

protein properties have limited ability to differentiate among the base supports. 

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Chapter 4 

• The base supports revealed less difference at high salt concentrations than at low salt 

concentrations. In other words, the differences among the base supports were less at 

high hydrophobic conditions than at low hydrophobic conditions. The reason could be 

the effects of mobile phase hydrophobic conditions. 

4.4. Effect of protein properties on separation behavior during chromatography 

• Several protein properties were investigated for the first time with the proteome wide 

approach for their effects on the protein retention behavior during HIC. 

• The average surface hydrophobicity (ASH), average hydrophobicity (AH) and average 

flexibility (AF) have revealed direct relationships with the retention behavior of the 

proteins in HIC. 

• The average bulkiness of the proteins did not show any relationship with the retention 

behavior during chromatography. 

• The polarity of the proteins exhibited an inverse relationship with the retention of the 

proteins. The higher the polarity of the protein, the less time it will stay on the 

adsorbent during chromatography and vice versa. 

• An indirect relationship was observed between polarity and hydrophobicity; proteins 

with high polarity were less hydrophobic in nature and vice versa. 

• An indirect relationship was also observed between polarity and flexibility; proteins 

with high polarity were less flexible in nature and vice versa. 

• Among all the properties, ASH was the only property which has a decisive role in the 

retention behavior of the proteins during HIC. 

• The salt and ASH ranges were defined for the first time during the chromatographic 

fractionation of the yeast cell proteome. The information gathered about ASH and the 

retention position during chromatography can be exploited for the chromatographic 

separation of recombinant proteins from host cell proteomes. 

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Chapter 4 

• The experimental data obtained with this proteome wide approach can be used to 

design the purification models utilizing the in silico approach. 

4.5. 2-D gels analysis 

• A high amount and a higher number of protein spots were reported at the beginning of 

the chromatography than at the end. This result confirmed the less hydrophobic nature 

of the S. cerevisiae cell proteome. 

• A higher number of smaller and basic proteins were reported than larger and acidic 

proteins which can further reflect the nature of the S. cerevisiae cell proteome. 

• The denaturation of proteins in HIC was confirmed with the proteome wide approach. 

• A large number of proteins were retained on Toyopearl-Butyl than Toyopearl-Phenyl, 

this confirmed that aromatic interactions have no effect on the retention behavior 

during HIC. 

• The effect of molecular weight of the proteins was studied in relation to the adsorbent 

type. The pore size and hydrophobicity of the adsorbents were concluded to be the 

main parameters responsible for the retention of larger proteins in HIC. 

• The effect of isoelectric point of the proteins was studied in relation to the adsorbent 

type. The neutral and acidic proteins were equally distributed over the 2-D gels. Basic 

proteins retained longer due to the presence of partial negative charges on the 

adsorbents which has given rise to ionic interactions in addition to hydrophobic 

interactions and resulted in longer retention of basic proteins. 

4.5. Additional conclusions 

• In total, 173 proteins were identified and the in-gel digestion protocol was optimized 

specifically for the proteins fractionated by HIC. The identified proteins can provide 

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Chapter 4 

additional understanding of the protein contaminants present in the yeast cell proteome 

and is a valuable addition to the proteomics technology. 

• Several important enzymes have been identified which were eluted at specific salt 

concentrations. These enzymes can be purified by a single isocratic chromatography 

without going through trial and error chromatographies to figure out the elution 

position of any enzyme. 

4.6. Recommendations for future work 

• The average protein properties derived from the primary structure need to be explored 

in reverse phase chromatography with the process proteomics approach. 

• The average polarity parameter can be calculated from the surface residues of the three 

dimensional structure of a protein. This average surface polarity can be used as an 

alternative parameter to ASH. 

• The adsorbent such as Toyopearl-Ether revealed an interesting chromatographic 

behavior and can be used in future for the purification of the recombinant proteins of 

highly hydrophobic nature. 

• This approach can be extended to other hydrophobic adsorbents and expression 

systems such as E.coli, mammalian cells and insect cells for the development of 

downstream processing. 

• This work involved the analysis of a real expression host instead of using model 

proteins and the data obtained can be used to design a purification model for the 

recombinant proteins utilizing an in silico approach. 

116

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