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