Using Protein Structure to Study
Network Pharmacology
Hauptman Woodward Institute
November 5, 2009
Philip E. Bourne
University of California San Diego
pbourne@ucsd.edu
Big Questions in the Lab
1.
2.
3.
4.
August 14, 2009
Can we improve how
science is disseminated
and comprehended?
What is the ancestry of the
protein structure universe
and what can we learn
from it?
Are there alternative ways
to represent proteins from
which we can learn
something new?
What really happens when
we take a drug?
Motivation
• The truth is we know very little about how the
major drugs we take work
• We know even less about what side effects
they might have
• Drug discovery seems to be approached in a
very consistent and conventional way
• The cost of bringing a drug to market is huge
>$800M
• The cost of failure is even higher e.g. Vioxx >$4.85Bn
Motivation
• The truth is we know very little about how the
major drugs we take work – receptors are
unknown
• We know even less about what side effects
they might have - receptors are unknown
• Drug discovery seems to be approached in a
very consistent and conventional way
• The cost of bringing a drug to market is huge
~$800M – drug reuse is a big business
• The cost of failure is even higher e.g. Vioxx $4.85Bn - fail early and cheaply
Why Don’t we Do Better?
A Couple of Observations
• Gene knockouts only effect phenotype in 1020% of cases , why?
– redundant functions
– alternative network routes
– robustness of interaction networks
A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690
• 35% of biologically active compounds bind to
more than one target
Paolini et al. Nat. Biotechnol. 2006 24:805–815
Implications
• Ehrlich’s philosophy of magic bullets
targeting individual chemoreceptors has
not been realized
• Stated another way – The notion of one
drug, one target, one disease is a little
naïve in a complex system
How Can we Begin to Address
the Problem?
• Systematic screening for multiple
targets
• Integration of knowledge from multiple
sources
• Analyze the impact on the complete
network
2. What is the ancestry of the
protein structure universe?
4. What really happens when
we take a drug?
Valas, Yang & Bourne 2009
Current Opinions in Structural Biology 19:1-6
Phosphoinositide-3 Kinase (D) and
Actin-Fragmin Kinase (E)
PKA
E. Scheeff and P.E. Bourne 2005 PLoS
Comp. Biol. 1(5): e49.
ChaK (“Channel Kinase”)
9
Implications
• The ATP binding cassette is preserved
yet the enzyme has evolved to bind a
variety of different substrates
• The evolutionary history of the protein
kinase-like superfamily can be traced
with careful analysis
• So taking this a step further …
The Role of Evolution
What if only the binding
pocket was conserved and the
global structure of the protein
has changed?
A drug could potentially bind to
distinctly different gene families
The Role of Evolution
If this is True What if…
• We can characterize a protein-ligand binding site
from a 3D structure (primary site) and search for that
site on a proteome wide scale?
• We could perhaps find alternative binding sites (offtargets) for existing pharmaceuticals and NCEs?
• We could use it for lead optimization and possible
ADME/Tox prediction
• We might be able to construct a site similarity
network for a given proteome to define multiple
targets for dirty drugs
The Role of Evolution
What Do Off-targets Tell Us?
•
One of four things:
1. Nothing
2. A possible explanation for a side-effect of a drug
3. A possible repositioning of a drug to treat a
completely different condition
4. A multi-target strategy to attack a pathogen
Today I will give you examples of 2, 3 and 4 while
illustrating the complexity of the problem
The Role of Evolution
Agenda
• Computational Methodology
• Side Effects - The Tamoxifen Story
• Repositioning an Existing Drug - The TB Story
• Salvaging $800M – The Torcetrapib Story
• The Future? - The TB Drugome
Need to Start with a 3D Drug-Receptor
Complex - The PDB Contains Many
Examples
Generic Name
Other Name
Treatment
PDBid
Lipitor
Atorvastatin
High cholesterol
1HWK, 1HW8…
Testosterone
Testosterone
Osteoporosis
1AFS, 1I9J ..
Taxol
Paclitaxel
Cancer
1JFF, 2HXF, 2HXH
Viagra
Sildenafil citrate
ED, pulmonary
arterial
hypertension
1TBF, 1UDT,
1XOS..
Digoxin
Lanoxin
Congestive heart
failure
1IGJ
Computational Methodology
A Reverse Engineering Approach to
Drug Discovery Across Gene Families
Characterize ligand binding
site of primary target
(Geometric Potential)
Identify off-targets by ligand
binding site similarity
(Sequence order independent
profile-profile alignment)
Extract known drugs
or inhibitors of the
primary and/or off-targets
Search for similar
small molecules
…
Dock molecules to both
primary and off-targets
Statistics analysis
of docking score
correlations
Computational Methodology
43,738
Human Proteins
map human proteins to
drug targets with BLAST
e-value < 0.001
map human proteins to
PDB structures with >95%
sequence identity
13,865
Human Proteins
(2,002 Drug Targets)
3,158
Human Proteins
(10,730 PDB Structures)
map drug targets to
PDB structures
1,585
PDB Structures
(929 Drug Targets)
cover 929/2,002 = 46.4%
drug targets structurally
remove redundant
structures with 30%
sequence identity
2,586
PDB Structures
remove redundant structures
with 30% sequence identity,
825
PDB Structures
(druggable)
What we Search Against
The Human Target List
Computational Methodology
Characterization of the Ligand Binding
Site - The Geometric Potential
 Conceptually similar to hydrophobicity
or electrostatic potential that is
dependant on both global and local
environments
• Initially assign Ca atom with
a value that is the distance
to the environmental
boundary
• Update the value with those
of surrounding Ca atoms
dependent on distances and
orientation – atoms within a
10A radius define i
GP  P 
Pi
cos(ai)  1.0

2.0
neighbors Di  1.0

Computational Methodology
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
Discrimination Power of the Geometric
Potential
4
binding site
non-binding site
3.5
• Geometric
potential can
distinguish
binding and
non-binding
sites
3
2.5
2
1.5
1
0.5
100
99
88
77
66
55
44
33
22
11
0
0
Geometric Potential
Computational Methodology
0
Geometric Potential Scale
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
Local Sequence-order Independent Alignment
with Maximum-Weight Sub-Graph Algorithm
Structure A
Structure B
LER
VKDL
LER
VKDL
• Build an associated graph from the graph representations of two
structures being compared. Each of the nodes is assigned with a
weight from the similarity matrix
• The maximum-weight clique corresponds to the optimum alignment
of the two structures
Xie and Bourne 2008 PNAS, 105(14) 5441
Similarity Matrix of Alignment
Chemical Similarity
• Amino acid grouping: (LVIMC), (AGSTP), (FYW), and
(EDNQKRH)
• Amino acid chemical similarity matrix
Evolutionary Correlation
• Amino acid substitution matrix such as BLOSUM45
• Similarity score between two sequence profiles
d   f a Sb   f b S a
i
i
i
i
i
i
fa, fb are the 20 amino acid target frequencies of profile a
and b, respectively
Sa, Sb are the PSSM of profile a and b, respectively
Computational Methodology
Xie and Bourne 2008 PNAS, 105(14) 5441
Nothing in Biology {Including
Drug Discovery} Makes Sense
Except in the Light of
Evolution
Theodosius Dobzhansky
(1900-1975)
Lead Discovery from Fragment
Assembly
• Privileged molecular moieties
in medicinal chemistry
• Structural genomics and high
throughput screening generate
a large number of proteinfragment complexes
• Similar sub-site detection
enhances the application of
fragment assembly strategies
in drug discovery
1HQC: Holliday junction migration motor protein
from Thermus thermophilus
1ZEF: Rio1 atypical serine protein kinase
from A. fulgidus
Computational Methodology
Lead Optimization from
Conformational Constraints
• Same ligand can bind to
different proteins, but with
different conformations
• By recognizing the
conformational changes in the
binding site, it is possible to
improve the binding specificity
with conformational constraints
placed on the ligand
1ECJ: amido-phosphoribosyltransferase
from E. Coli
1H3D: ATP-phosphoribosyltransferase
from E. Coli
Computational Methodology
Scoring
The Point is this Approach Can Now be Applied
on a Proteome-wide Scale
a)
b)
Blosum45 and
b) McLachlan substitution matrices.
• Scores for binding site
matching by SOIPPA follow
an extreme value distribution
(EVD). Benchmark studies
show that the EVD model
performs at least two-orders
faster and is more accurate
than the non-parametric
statistical method in the
previous SOIPPA version
Xie, Xie and Bourne 2009 Bioinformatics 25(12) 305-312
Computational Methodology
Agenda
• Computational Methodology
• Side Effects - The Tamoxifen Story
• Repositioning an Existing Drug - The TB Story
• Salvaging $800M – The Torcetrapib Story
• The Future? - The TB Drugome
Found..
• Evolutionary linkage between:
– NAD-binding Rossmann fold
– S-adenosylmethionine (SAM)-binding domain of SAMdependent methyltransferases
• Catechol-O-methyl transferase (COMT) is SAMdependent methyltransferase
• Entacapone and tolcapone are used as COMT
inhibitors in Parkinson’s disease treatment
• Hypothesis:
– Further investigation of NAD-binding proteins may
uncover a potential new drug target for entacapone
and tolcapone
Repositioning an Existing Drug - The TB Story
Functional Site Similarity between
COMT and InhA
• Entacapone and tolcapone docked onto 215 NADbinding proteins from different species
• M.tuberculosis Enoyl-acyl carrier protein reductase ENR
(InhA) discovered as potential new drug target
• InhA is the primary target of many existing anti-TB drugs
but all are very toxic
• InhA catalyses the final, rate-determining step in the fatty
acid elongation cycle
• Alignment of the COMT and InhA binding sites revealed
similarities ...
Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
Repositioning an Existing Drug - The TB Story
Binding Site Similarity between
COMT and InhA
COMT
SAM (cofactor)
BIE (inhibitor)
InhA
NAD (cofactor)
641 (inhibitor)
Repositioning an Existing Drug - The TB Story
Summary of the TB Story
• Entacapone and tolcapone shown to have potential
for repositioning
• Direct mechanism of action avoids M. tuberculosis
resistance mechanisms
• Possess excellent safety profiles with few side effects
– already on the market
• In vivo support
• Assay of direct binding of entacapone and tolcapone
to InhA reveals a possible lead with no chemical
relationship to existing drugs
Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
Repositioning an Existing Drug - The TB Story
Summary from the TB Alliance
– Medicinal Chemistry
• The minimal inhibitory concentration
(MIC) of 260 uM is higher than usually
considered
• MIC is 65x the estimated plasma
concentration
• Have other InhA inhibitors in the
pipeline
Repositioning an Existing Drug - The TB Story
Agenda
• Computational Methodology
• Side Effects - The Tamoxifen Story
• Repositioning an Existing Drug - The TB Story
• Salvaging $800M – The Torcetrapib Story
• The Future? - The TB Drugome
Selective Estrogen Receptor
Modulators (SERM)
• One of the largest
classes of drugs
• Breast cancer,
osteoporosis, birth
control etc.
• Amine and benzine
moiety
PLoS Comp. Biol., 2007 3(11) e217
Side Effects - The Tamoxifen Story
Adverse Effects of SERMs
cardiac abnormalities
thromboembolic
disorders
loss of calcium
homeostatis
?????
ocular toxicities
PLoS Comp. Biol., 3(11) e217
Side Effects - The Tamoxifen Story
Structure and Function of SERCA
Sacroplasmic Reticulum (SR) Ca2+ ion channel
ATPase
• Regulating cytosolic
calcium levels in
cardiac and skeletal
muscle
• Cytosolic and
transmembrane
domains
PLoS Comp. Biol., 3(11) e217
• Predicted SERM
binding site locates in
the TM, inhibiting Ca2+
uptake
Side Effects - The Tamoxifen Story
Binding Poses of SERMs in
SERCA from Docking Studies
• Salt bridge
interaction between
amine group and
GLU
• Aromatic
interactions for both
N-, and C-moiety
6 SERMS A-F (red)
PLoS Comp. Biol., 3(11) e217
Side Effects - The Tamoxifen Story
The Challenge
• Design modified SERMs that bind as
strongly to estrogen receptors but do
not have strong binding to SERCA, yet
maintain other characteristics of the
activity profile
PLoS Comp. Biol., 3(11) e217
Side Effects - The Tamoxifen Story
Agenda
• Computational Methodology
• Side Effects - The Tamoxifen Story
• Repositioning an Existing Drug - The TB Story
• Salvaging $800M – The Torcetrapib Story
• The Future? - The TB Drugome
PLoS Comp Biol 2009 5(5) e1000387
The Torcetrapib Story
Cholesteryl Ester Transfer Protein (CETP)
CETP inhibitor
X
CETP
LDL
HDL
Bad Cholesterol
Good Cholesterol
• collects triglycerides from very low density or low density lipoproteins
(VLDL or LDL) and exchanges them for cholesteryl esters from high
density lipoproteins (and vice versa)
• A long tunnel with two major binding sites. Docking studies suggest
that it possible that torcetrapib binds to both of them.
• The torcetrapib binding site is unknown. Docking studies show that
both sites can bind to torcetrapib with the docking score around -8.0.
PLoS Comp Biol 2009 5(5) e1000387
The Torcetrapib Story
Docking Scores eHits/Autodock
Off-target
PDB Ids
Torcetrapib
Anacetrapib
JTT705
Complex ligand
CETP
2OBD
-11.675 / -5.72
-11.375 / -8.15
-7.563 / -6.65
-8.324 (PCW)
Retinoid X receptor
1YOW
1ZDT
-11.420 / -6.600
-6.74
-8.696 / -7.68
-7.35
-6.276 / -7.28
-6.95
-9.113 (POE)
PPAR delta
1Y0S
-10.203 / -8.22
-10.595 / -7.91
-7.581 / -8.36
-10.691(331)
PPAR alpha
2P54
-11.036 / -6.67
-0.835 / -7.27
-9.599 / -7.78
-11.404(735)
PPAR gamma
1ZEO
-9.515 / -7.31
> 0.0 / -8.25
-7.204 / -8.11
-8.075 (C01)
Vitamin D receptor
1IE8
>0.0/ -4.73
>0.0 / -6.25
-6.628 / -9.70
-8.354 (KH1) -7.35
Glucocorticoid
Receptor
1NHZ
1P93
Fatty acid
binding protein
2F73
2PY1
2NNQ
>0.0/ -4.33
>0.0/-6.13
/-6.40
>0.0/ -7.81
>0.0/ -6.98
/-7.64
-7.191 / -8.49
/-6.33
/6.35
???
T-Cell CD1B
1GZP
-8.815 / -7.02
-13.515 / -7.15
-7.590 / -8.02
-6.519 (GM2)
IL-10 receptor
1LQS
/ -4.59
/ -6.77
GM-2 activator
2AG9
-9.345 / -6.26
-9.674 / -6.98
(3CA2+) CARDIAC
TROPONIN C
1DTL
/-5.83
/-6.71
/-5.79
cytochrome bc1
complex
1PP9 (PEG)
/-6.97
/-9.07
/-6.64
1PP9 (HEM)
/-7.21
/8.79
/-8.94
1V5H
/-4.89
/-7.00
/-4.94
human cytoglobin
PLoS Comp Biol 2009 5(5) e1000387
/-4.43
/-5.63
/-7.08
/-0.58
/-7.09
/-9.42
/ -5.95
-8.617 / -6.17
???
??? (MYR) -4.16
The Torcetrapib Story
JTT705
Torcetrapib
Anacetrapib
JTT705
VDR
–
RAS
RXR
+
PPARα
PPARδ
FA
?
FABP
?
?
PPARγ
+
High blood
pressure
Anti-inflammatory
function
PLoS Comp Biol 2009 5(5) e1000387
JNK/IKK pathway
JNK/NF-KB pathway
Immune response
to infection
The Torcetrapib Story
Agenda
• Computational Methodology
• Side Effects - The Tamoxifen Story
• Repositioning an Existing Drug - The TB Story
• Salvaging $800M – The Torcetrapib Story
• The Future? - The TB Drugome
Existing Drugs
3. Protein-ligand
Docking
Structural
Proteome
2. Binding site
Similarity
…
Protein-drug
Interactome
Drugome
Target identification
1. Structural
Determination
& Modeling
Genome
4.2 Network
Integration
4.1 Network
Reconstruction
Metabolome
Drug repurposing
Side effect prediction
New therapeutics
Drug resistance
mechanism
Bioinformatics 2009 25(12) 305-312
The Future
Existing Drugs
3. Protein-ligand
Docking
TB Structural
Proteome
…
TB Protein-drug
Interactome
2. Binding site
Similarity
Drugome/TB
1. Structural
Determination
& Modeling
TB Genome
4.2 Network
Integration
4.1 Network
Reconstruction
TB Metabolome
Target identification
Drug repurposing
Side effect prediction
New therapeutics
for MDR and XDR-TB
Drug resistance
mechanism
Bioinformatics 2009 25(12) 305-312
The TB Druggome
Predicted protein-ligand interaction network of M.tuberculosis. Proteins that are
predicted to have similar binding sites are connected. Squares represent the
top 18 most connected proteins.
Bioinformatics 2009 25(12) 305-312
The TB Druggome
Bioinformatics 2009 25(12) 305-312
The TB Druggome
Some Limitations
• Structural coverage of the given
proteome
• False hits / poor docking scores
• Literature searching
• It’s a hypothesis – need experimental
validation
• Money 
Limitations
Summary
• We have established a protocol to look for offtargets for existing therapeutics and NCEs
• Understanding these in the context of
pathways would seem to be the next step
towards a new understanding –
cheminfomatics meets systems biology
• Lots of other opportunities to examine
existing drugs – DrugX and the Recovery Act
Bioinformatics Final
Examples..
• Donepezil for treating Alzheimer’s
shows positive effects against other
neurological disorders
• Orlistat used to treat obesity has proven
effective against certain cancer types
• Ritonavir used to treat AIDS effective
against TB
• Nelfinavir used to treat AIDS effective
against different types of cancers
Lots of Opportunities
Acknowledgements
Lei Xie
Li Xie
Jian Wang
Sarah Kinnings
http://funsite.sdsc.edu