Evotec Munich

evotec.com

Evotec Munich

Evotec Munich

Building innovative

drug discovery alliances

Chemical Proteomics and Quantitative Mass Spectrometry

to Support the Discovery & Development of Targeted Drugs

Evotec AG, Evotec Munich, May 2012


About Evotec Munich

A leader in chemical proteomics and quantitative mass spectrometry

Evotec’s Center of Excellence for Proteomics and Oncology

• Emerged from Kinaxo Biotechnologies GmbH, a Max Planck spin-off founded by the renowned cancer

researcher Prof. Axel Ullrich

• Collaborates with leading academic research laboratories including the lab of Prof. Matthias Mann at the

Max Planck Institute

• Combines highest service quality standards with powerful technological innovation developed by leading

proteomics scientists such as Dr. Henrik Daub, Evotec’s VP Technology & Science

PAGE 1 Selected publications by Evotec Munich scientists and Evotec Munich scientific advisors


PAGE 2

Evotec Munich

Technology Offering


PAGE

State of the art proteomics technologies

Support development of targeted drugs

Cellular Target Profiling ® Subproteomic Cellular Target Profiling ®

3

Target

discovery

Target ID &

validation

Scaffold

selection /

HT

screens

Screening

Early stop-loss

decision for scaffold

selection

On/off

target

profiling

Mode of

Action

analysis

(KinAffinity ® , Epigenetics Target Profiling ® )

Response

Prediction

Biomarker

Assays

H2L LO Preclinic PhI/II

Cellular Profiling to

accompany H2L

Selectivity analysis of lead

compounds

PTM signatures as

predictive biomarkers

Omics Technologies (Phosphoproteomics, Acetylomics)


PAGE 4

Cellular Target Profiling

Revelation of a compound’s cellular target spectrum

• Determinates a compound‘s proteome-wide binding affinities in any cell line or

tissue of choice

• State-of-the-art chemical proteomics facilitates unbiased native profiling against

endogenously expressed, full length proteins in the presence of cellular co-factors and complex partners

• Adresses previously unavailable targets in combination with phenotypic screens and thus broadens the

available chemical space for drug discovery

• Leads to compounds with a relevant efficacy profile, ready for in vivo POC studies

• Extensive, non-target class restricted track record in target deconvolution and profiling of various small

molecule compounds (e.g. kinase inhibitors, antibiotics, epigenetic drugs, small molecules targeting

metabolic enzymes, ligases, reductases, transferases, heat shock proteins, cyclooxygenases)


PAGE

Light Medium Heavy

Cellular Target Profiling

Workflow

Arg6 /Lys4 Arg0 /Lys0 Arg10 /Lys8 LC-MS/MS (1)

Proteome labeling

Metabolic

Chemical

Compound / target binding

Labeled lysates are incubated with

immobilized compound at different densities

of coupled compound.

Different concentrations of soluble compound

are added to the mixture of labeled lysate at a

fixed density of coupled compound.

Identification and quantification of proteins

Identification and determination of the relative amounts of the

captured proteins by liquid chromatography and quantitative mass

spectrometry

Determination of K d,free values

Generation of compound/target curves for immobilized and free

compound. Application of Cheng-Prusoff equation to determine

the K d,free values of all target proteins

Light Medium Heavy

Arg6 /Lys4 Arg0 /Lys0 Arg10 /Lys8 LC-MS/MS (2)

5 Sharma et al., Nat Methods. 2009 Oct;6(10): 741-4

Patent Protection EP2045332B1


Kd [µM]

0.01

0.1

1.0

10

PAGE 6

PI3-kinase catalytic subunit delta (PIK3CD)

PI3-kinase catalytic subunit beta (PIK3CB)

PI3-kinase regulatory subunit beta (PIK3R 2)

PI3-kinase regulatory subunit gamma (PIK3R3)

Beclin-1 (BECN1)

PI3-kinase regulatory subunit 4 (PIK3R4)

PI3-kinase catalytic subunit type 3 (PIK3C3)

UV radiation resistance-assoc. gene protein (UVRAG)

PI4-kinase catalytic subunit alpha (PI4KA)

PI3-kinase catalytic subunit alpha (PIK3CA)

Glycogen synthase kinase-3 alpha (GSK3A)

Adenosine kinase (ADK)

Zn-bdg alcohol dehydrogenase domain-cont. prot. 2

Diphthine synthase (DPH5)

Glycogen synthase kinase-3 beta (GSK3B)

Serine/threonine-protein kinase RIO2 (RIOK2)

Ketosamine-3-kinase (FN3KRP)

Pyridoxal kinase (PDXK)

Lactoylglutathione lyase (GLO1)

Isochorismatase domain-containing protein 2 (ISOC2)

Phospholipase D3 (PLD3)

Deoxycytidine kinase (DCK)

DNA-dep. protein kinase catalytic subunit (PRKDC)

Cellular Target Profiling

Profiling of a kinase inhibitor

Protein kinases

Kinase assoc. proteins

Non-kinase proteins

PIK3CD

PIK3CB

PIK3C3

PIK3CA

K d,free = 87 nM

K d,free = 300 nM

K d,free = 800 nM

K d,free = 1366 nM

UCB’s cpd discriminates PI3K isoforms within a

cancer cell line


0,01

0,1

1

10

K d [µM]

PAGE 7

UNC0638 BIX-01294

N

O

O

N

WIZ

GLP

G9a

Protein X

OCR

NH

N

N

Cellular Target Profiling

Profiling of methyltransferase inhibitors

N

H

N

WIZ

GLP

G9A

N N

N

O

O

Cellular target proteins for the published G9a selective

methyltransferase inhibitors UNC0638 and BIX-01294 were

identified in human CML (K562) cells

Comparison with published in vitro data revealed known as well

as previously unknown target proteins for both compounds

Protein UNC0638

K dµM

Evotec

UNC0638

IC 50µM

Vedadi et al

BIX-01294

K dµM

Evotec

BIX-01294

IC 50µM

Vedadi et al

WIZ 0,039 - 0,056 -

GLP 0,099 0,019 0,101 0,034

Protein X 0,270 - -

G9A 0,152 < 0,015 0,294 0,180

OCR 4,524 - - -

WIZ Widely-interspaced zinc finger-containing protein, mediating EHMT1 / EHMT2 interaction

Protein X Possible UNC0638 off-target protein, validation experiments ongoing

OCR Spindlin-1 / Ovarian cancer-related protein

Vedadi et al, 2007, Nat Chem Biol., 7:566-74


0,1

1

10

K d [µM]

PAGE 8

Parafibromin (CDC73)

HSP90alpha (HSP90AA1)

Probable Xaa-Pro aminopeptidase 3 (XPNPEP3)

Putative HSP90beta 4 (HSP90AB4P)

HSP90beta (HSP90AB1)

Endoplasmin (HSP90B1)

1-phosphatidylinositol-4,5-bisphosphate

phosphodiesterase eta-1 (PLCH1)

Adenosine kinase (ADK)

Cellular Target Profiling

Profiling of the geldanamycin derivative 17-DMAG

HSP90 protein family

Other protein

Cellular Target Profiling confirmed Hsp90 as prime target for

17-DMAG, which is consistent with literature data

In total, four members of the Hsp90 family were identified and

K d values of the highly homologous isoforms Hsp90-alpha and

Hsp90-beta were determined. Only very few other protein

targets were identified, indicating high specificity of 17-DMAG

for Hsp90.

17-DMAG

(17-dimethyl-amino-ethylamino-17-demethoxygeldanamycin)


Protein

Name

PAGE 9

Sequence

Coverage

[%]

Cellular Target Profiling

Target deconvolution of 3 related compounds with different cellular activity

Cancer cell viability Compound 1 < Compound 2 < Compound 3

EC 50 ~ 50 nM ~ 150 nM ~ 1µM

Binding Curve

(Linker

Compound)

120

100

80

Competition

Cpd 1

K dfree [M]

Cpd 1

Competition

Cpd 2

K dfree [M]

Cpd 2

Competition

Cpd 3

60

60

60

60

Protein Y 64,9 40

40

0,146 40

1,06 1,3

40

% Bound

20

0

-20

120

100

80

10 1

Conc [µM]

10 2

K dfree [M]

Cpd 3

60

60

60

60

40

Protein E 66,9 40

40

0,195 40

0,308 0,329

% Bound

% Bound

20

0

-20

120

100

80

10 1

Conc [µM]

10 2

60

80

80

60

40

60

60

40

Protein F 60,7 - 3,2 3,42

20

40

40

0

-20

120

100

80

10 1

Conc [µM]

10 2

80

Protein G 71,3

60

40

60

40

-

60

40

3,63

60

40

3,91

% Bound

20

0

-20

10 1

Conc [µM]

10 2

% Bound

% Bound

% Bound

% Bound

120

100

80

20

0

-20

120

100

80

20

0

-20

140

120

100

20

0

-20

120

100

20

0

-20

10 -2

10 -2

10 -2

10 -2

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

10 1

10 1

10 1

10 1

Protein Y is most likely the relevant cellular target

Observed phenotypes are consistent with literature data about target MoA

% Bound

% Bound

% Bound

% Bound

120

100

80

20

-20

120

100

80

20

0

-20

140

120

100

-20

120

100

0

20

80

20

-20

0

0

10 -2

10 -2

-2

10

-2

10

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

-1

10

Conc [µM]

-1

10

Conc [µM]

10 1

10 1

0 1

10 10

0 1

10 10

% Bound

% Bound

% Bound

% Bound

120

100

80

20

-20

120

100

80

20

0

-20

0

120

100

80

20

0

-20

120

100

80

20

0

-20

10 -2

10 -2

10 -2

10 -2

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

10 -1

10 0

Conc [µM]

10 1

10 1

10 1

10 1


PAGE 10

Cellular Target Profiling in sub-proteomes

Cellular profiling of kinase inhibitors and epigenetics drugs

• Fast, reliable profiling of sub-proteomes of interest

• High quality native selectivity data for kinase inhibitors or epigenetic drugs

• Correlation of in vivo data with native target information to support the selection of drug candidates in a

variety of medical indications

• KinAffinity ® facilitates profiling against more than 300 native kinases

• KinAffinity ® allows for identification of additional kinase targets not

detectable by kinase panel screening

• Epigenetics Target Profiling ® facilitates profiling of epigenetics

drugs under native conditions

Extensive kinome coverage


Extensive Kinome Coverage

PAGE 11

KinAffinity

Fast profiling of endogenously expressed kinases using a generic matrix

Using KinAffinity , more than

300 protein kinases from all

kinase families can be enriched

for cellular profiling (red dots).

On average, 100-200 kinases

can be profiled in one cell-line /

per single experiment

Kinase domain sequences were taken from Manning, G. et al., The protein kinase complement of

the human genome. Science, 298 (2002), 1912-34. The kinome tree was constructed using

ClustalW2 [Larkin, MA. et al., Clustal W and Clustal X version 2.0., Bioinformatics, 23 (2007),

2947-8] and Dendroscope [Huson, DH. et al., Dendroscope: An interactive viewer for large

phylogenetic trees. BMC Bioinformatics, 8 (2007), 460].


0,01

0,1

1

10

K d[µM]

PAGE 12

MAP4K5

GAK

ABL2

ABL1

LYN

MAP4K3

FYN

SRC

MAP4K4

FRK

CSK

QIK

EphA2

EphB4

MAP2K2

ACK

TNIK

QSK

SLK

MAP2K1

MAP3K2

LOK

YES

MYT1

ZAK

EphB2

FAK

MAP3K4

GCN2

CK1d

FER

IKKe

MST4

MST3

MST1

SYK

MAP2K6

PYK2

LRRK2

ILK

MST2

TLK2

Target profile of bosutinib in PC3 cells

KinAffinity

Native kinome profiling

Bosutinib (SKI606, Wyeth) is currently tested in breast

cancer and CML

KinAffinity ® enriched nearly 200 endogenously expressed

kinases from human prostate cancer (PC3) cells

45 of these kinases were identified as molecular targets of

bosutinib

N

O

N O N

Cl Cl

HN O

N


0,01

0,1

1

K d[µM]

PAGE 13

DDR1

BCR

ABL1

ABL2

PIP4K2A

PIP4K2C

Target profile of imatinib in K562 cells

KinAffinity

Native kinome profiling of type II inhibitors

Imatinib (Gleevec , Novartis) is approved for the treatment of

CML and GIST and is known to inhibit several tyrosine

kinases

KinAffinity identified imatinib’s main kinase targets with the

exception of PDGFR, which is not expressed in K562 cells

N

N

N

N NH

H

O

N

N


0,1

1

10

K d M

PAGE 14

K d [µM]

MIER3

C16orf87

SIN3A

SIN3B

MIER1

NCOR1

RCOR2

NCOR2

MIER2

CHD3

GPS2

BHC80

ZNF217

GSE1

BRAF35

CoREST

HDAC1

RERE

ZNF261

AOF2

GATAD2A

RCOR3

HDAC2

IRA1

WDR5

MTA3

TBL1

GATAD2B

CHD4

MTA1

MBD3

RBBP4

HMG20A

HDAC3

MTA2

ISOC2

ZNF198

RBBP7

HDAC6

DBP5

MBD2

KPNA3

PP3476

HDAC10

KPNA4

LAMB2

ALDH2

MBLAC2

CK2a1

ALDH1B1

TTC38

HDAC8

Epigenetics Target Profiling

Profiling of SAHA (Vorinostat) using a generic matrix

80

80

60

60

40

40

HDAC1 20

20

0,19

120

100

100

80

80

60

60

40

HDAC4 40

-

20

20

% Bound

0

-20

120

20

0

-20

1

10 102

Conc [µM]

120

120

100

100

80

80

60

60

40

HDAC5 40

-

20

20

100

100

80

80

60

60

HDAC7 -

40

40

% Bound

% Bound

0

-20

120

20

0

-20

1

10 102

Conc [µM]

120

100

100

80

80

60

60

HDAC6 40

0,43

40

1

10 102

Conc [µM]

120

100

100

80

80

60

60

HDAC8 6,5

40

40

% Bound

120

20

0

-20

1

10 102

Conc [µM]

100

100

80

80

60

60

HDAC10 0,79

40

40

% Bound

% Bound

% Bound

120

100

0

-20

120

100

0

-20

120

20

0

-20

1

10 102

Conc [µM]

80

80

60

60

40

40

HDAC2 0,24

20

20

% Bound

120

100

0

-20

1

10 102

Conc [µM]

80

80

60

60

40

40

HDAC3 0,35

20

20

HDAC

Known HDAC complex partner

Other proteins

Target profile in human ovarian cancer (A2780) cells

% Bound

Binding Competition K d M

1

10 102

Conc [µM]

1

10 102

Conc [µM]

1

10 102

Conc [µM]

% Bound

% Bound

% Bound

% Bound

% Bound

% Bound

% Bound

% Bound

% Bound

120

100

0

-20

120

100

0

-20

120

100

0

-20

120

0

-20

0

-20

20

0

-20

120

20

0

-20

20

0

-20

120

20

0

-20

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1

Conc [µM]

100 101

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1 100 101

Conc [µM]

-2

10 10-1 100 101

Conc [µM]

Epigenetics Target Profiling allows for

maximum HDAC coverage and constitutes

a native assay under conditions that

preserve the integrity of HDAC complexes


PAGE 15

Quantitative proteomics and in vivo PTM analysis

Mode of Action analysis of targeted drugs & biomarker identification

• High-end mass spectrometry and software applications facilitate quantitative analyses of the complete

proteome or protein modifications such as phosphorylation or acetylation in living cells, tissue or

patient samples

• Monitoring of global protein expression changes and comprehensive investigation of signaling

pathways to determine the influence of drug treatment, age, disease state or mutation status on

biological systems

• Applications include mode of action analysis of targeted

drugs and discovery of biomarkers (protein

expression, phosphorylation, acetylation) for patient

stratification

Non-responder Responder

P

P

P

P

P

gene expression

proliferation

P

P

Biomarker

P

P

P

P

P

P

gene expression

proliferation

P

P

Biomarker

P


PAGE 16

Complete Proteome Analysis

Large-scale, label-free quantification of proteins

Recent advances in sample processing and mass spectrometry enables fast

identification of ~7,000 proteins per cell line

In a comparative proteomics analysis co-published by Evotec Munich scientists,

more than 10,000 proteins were identified across 11 cell lines (this corresponds to

51% of the human genes)

Comparisons with deep sequencing data indicate this

covers most of the proteome expressed in human cancer

cell lines

Proteome profiling has thus reached a level of

comprehensiveness similar to mRNA profiling

Geiger et al, 2012,, MCP, 11, M111 014050

Schaab et al., 2012, MCP, 11, M111 014068


PAGE 17

PhosphoScout ®

Mode of Action analysis of targeted drugs & biomarker identification

Quantitative mass spectrometry to facilitate unbiased monitoring of dynamic phosphorylation events in vivo

on a global scale

Reliable measurement of more than 15,000 phosphorylation sites in a single experiment

Isotopic labeling

protein

preparation

P

enzymetic

cleavage

SCX

Global phosphoproteome

enrichment

LC-MS/MS Data Analysis

I

MS

m/z

MaxQuant Software

(Dept. M. Mann, MPI)

Unbiased, global quantitative

phosphoproteome analysis

I

MS/MS

m/z

G-V-S-P-A-W-R

P

PhosphoScout ® Workflow

P


PAGE 18

PhosphoScout ® - Case Study Sorafenib (Nexavar ® )

Experimental design & Identification of regulated phosphorylation sites

Control Treated (30 min) Treated (90 min)

PhosphoScout ® workflow

Sorafenib

Phosphorylations from 3 biological replicates All P-sites

No. of detected phosphorylation sites 15,825

No. of detected proteins with phosphorylation sites 3,931

No. of regulated sites 1,012

No. of proteins with regulated phosphorylation sites 605

PC-3 (human prostate

cancer) cells

Only class I sites (phosphorylation sites that could be localized

within the peptide sequence with high confidence ) were used

in our pathway analysis

Identification of differentially regulated phosphorylation sites at

a false discovery rate of 5% based on a global rank test


MAPK-Pathway

PhosphoScout ® - Case Study Sorafenib (Nexavar ® )

Data analysis - Identification of regulated protein networks

mTOR Pathway

The „SubExtractor“ algorithm integrates

phosphoproteomics data with information

from STRING protein/protein interactions

to unbiasedly identify differentially

regulated subnetworks

PAGE 19 Klammer et al., 2010, BMC Bioinformatics,11:351


N

O

N

PAGE 20

O

N

O

N

F F

F

Cl

PhosphoScout ® - Case Study Sorafenib (Nexavar ® )

Data analysis – Analysis of regulated networks

Translation

P

P

10x up-regulated

not regulated

10x down-regulated


PAGE 21

Acetylomics

Mode of Action analysis of targeted drugs

Comprehensive mass spectrometric analysis of global cellular

acetylation in relevant biological samples

Identification of novel acetylation sites and correlation of known

acetylation effects with a drug‘s mechanism of action

Acetylomics is an ideal tool to extend the mechanistic relevance and

research interest in HDACs well beyond the field of chromatin

biology

Combination with Evotec Munich‘s additional services such as global

phosphoproteomics allows for a unique approach to study a drug‘s

influence on a variety of post translational modifications

Identification of regulated acetylation networks

mean ratio treatment A

mean ratio treatment B

Comparison of differentially regulated

acetylation-sites in dependence of different

treatment conditions


Test sample set

(cell lines, xenografts,

patient samples)

PAGE 22

Proteome and Phosphoproteome Biomarker

Discovery – Complementary Approaches

Measurement of 10.000+ Phosphosites and/or 6.000+ Proteins

Quantitative

phosphoproteome

profiling

Quantitative

proteome

profiling

responder

non-resp.

responder

non-resp.

PTM sites

Protein expression

Global analysis of cellular kinase

activity

Direct linkage with kinase function

Analyses of other PTMs possible, such

as lysine acetylation for HDACs

Global analysis of cellular protein

levels

Target class independent

Adapted to small sample sizes

Easy transition from discovery to

validation/routine analysis


PAGE 23

Summary

Evotec Munich provides state-of-the art Cellular Target Profiling ® to identify a small molecule‘s

on/off-target interactions on a proteom wide level or on subproteomic target classes of interest

Quantitative mass spectrometry is employed to monitor dynamic post translational modification

events in vivo on a global scale that enables correlation of known PTM effects with a drug‘s

mechanism of action

Evotec Munich offers service technologies that facilitate a comprehensive cellular global

mode of action analysis of targeted drugs


Your contact:

proteomics@evotec.com

Building innovative

drug discovery alliances

More magazines by this user
Similar magazines