A Systematic Approach to Identifying
Protein-Ligand Binding Profiles on a
Proteome Scale and its Application
to Pharmaceutical Sciences
Philip E. Bourne
University of California San Diego
pbourne@ucsd.edu
Support Open Access
AFP 2008
What I Will Cover Today
• While we are interested
in automated function
prediction, we are
particularly interested in
how AFP techniques
can be applied to the
pharmaceutical
sciences – this is what I
will cover today
proteome.sdsc.edu
More Specifically…
• If we can predict where a ligand might
bind to a receptor protein not only have
we learnt something about the function
of that protein, if that ligand happens to
have therapeutic properties we might
learn something about the primary and
secondary targets of that compound
Why Bother?
• 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 - Hence fail early and cheaply
Our Approach
Nothing in Biology {Including
Functional Prediction & Hence Drug
Discovery} Makes Sense
Except in the Light of Evolution
Theodosius Dobzhansky (1900-1975)
What Has Evolution Taught Us?
• Global 3D similarity and sequence
similarity do not tell the whole story
• Perhaps a ligand binding site is what
has passed from generation to
generation while virtually all other
aspects of the protein have changed?
What Has Evolution Taught Us
About Drug Discovery?
• If that were true and evolutionarily related
ligand binding sites could be found, they
presumably would exist across very diverse
gene families
• From the perspective of drug discovery such
sites would have significant implications
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 (off-targets) for existing
pharmaceuticals?
• We could use it for lead optimization
and possible ADME/Tox prediction
What Do Off-targets Tell Us?
•
One of three 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
Today I will give you examples of both 2 and 3 and
illustrate the complexity of the problem
Agenda
• Computational Methodology
• Side Effects - The Tamoxifen Story
• Repositioning an Existing Drug - The TB
Story
• Salvaging $800M – The Torcetrapib Story
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
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
Characterization of the Ligand Binding
Site - Conceptual
1
2
ab
3
c
4
5
Computational Methodology
1. Represent the protein
structure
2. Determine the
environmental
boundary
3. Determine the protein
boundary
4. Computation of the
geometric potential
5. Computation of the
virtual ligand
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
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
Boundary Accuracy of Ligand Binding Site
Prediction
25
70
60
20
Distribution (%)
Distribution (%)
50
15
10
40
30
20
5
10
0
0
10
20
30
40
50
60
70
Sensitivity (%)
80
90 100
10
20
30
40
50
60
70
80
90 100
Specificity (%)
• ~90% of the binding sites can be identified with above 50%
sensitivity
• The specificity of ~70% binding sites identified is above 90%
Computational Methodology
So Far…
• Geometric potential dependant on local
environment of a residue – relative to other
residues and the environmental boundary
• Geometric potential reasonably good at
discriminating between ligand binding sites
and non-ligand binding sites
• Boundary of the binding site reasonably well
defined
• How to compare sites ???
Computational Methodology
Identification of Functional Similarity with
Local Sequence Order Independent Alignment
• Geometric and graph characterization of the
protein structure
• Chemical similarity matrix and evolutionary
relationship with profile-profile comparison
• Optimum alignment with maximum-weight subgraph algorithm
Computational Methodology
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
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
So What is the Potential of
this Methodology?
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
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
Finding Secondary Binding Sites
for Major Pharmaceuticals
• Scan known binding sites for major
pharmaceuticals bound to their
receptors against the human and other
“druggable” proteomes
• Filter!
• Try and correlate strong hits with known
data from the literature, databases,
clinical trials etc. to provide molecular
evidence of secondary effects
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)
Agenda
• Computational Methodology
• Repositioning an Existing Drug - The TB
Story
• Side Effects - The Tamoxifen Story
• Salvaging $800M – The Torcetrapib Story
Tuberculosis (TB)
•
•
•
•
One third of global population infected
Kills 2 million people each year
95% of deaths in developing countries
Anti-TB drugs hardly changed in 40
years
• MDR-TB and XDR-TB pose a threat to
human health worldwide
• Development of novel, effective, and
inexpensive drugs is an urgent priority
Repositioning an Existing Drug - The TB Story
Hypothesis Drawn from the Study
of Evolution
• We were looking for connections
(evolutionary linkages) across fold
and functional space through an allby-all comparison of ligand binding
sites
Repositioning an Existing Drug - The TB Story
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 ENR
• 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
• ENR is the primary target of many existing anti-TB drugs
but all are very toxic
• ENR catalyses the final, rate-determining step in the fatty
acid elongation cycle
• Alignment of the COMT and ENR binding sites revealed
similarities ...
Repositioning an Existing Drug - The TB Story
Binding Site Similarity between
COMT and ENR
COMT
SAM (cofactor)
BIE (inhibitor)
ENR
NAD (cofactor)
641 (inhibitor)
Repositioning an Existing Drug - The TB Story
2D Small Molecule (Dis)-Similarity
Between Existing and Potential ENR
Inhibitors
• Entacapone and tolcapone molecules
compared to 22 existing ENR inhibitors
• Background distributions created from a
random sample of 15,000 drug-like
molecules
• P-values calculated – not significant
• Unlikely that conventional screening methods
would identify entacapone and tolcapone as
potential ENR inhibitors
Repositioning an Existing Drug - The TB Story
Docked Existing and Potential ENR
inhibitors onto COMT and ENR
• Entacapone, tolcapone and existing ENR
inhibitors docked onto ENR and COMT,
respectively
• Predicted binding affinities of entacapone and
tolcapone towards ENR fell within the range of
binding affinities exhibited by known ENR
inhibitors
• Cross-reactivity between ENR and COMT
demonstrated
Repositioning an Existing Drug - The TB Story
In Vivo Studies
• Quantitative and microplate assays of
Mtb agree
• Entacapone - 80% growth inhibition with
62 ug/ml; 100% inhibition with 2x the
dose
• Tolcapone – similar results
Repositioning an Existing Drug - The TB Story
Courtesy Nancy Buchmeier
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
• At least some in vivo support
• Assay of direct binding of entacapone and
tolcapone to ENR under way
Repositioning an Existing Drug - The TB Story
Agenda
• Computational Methodology
• Repositioning an Existing Drug - The TB
Story
• Side Effects - The Tamoxifen Story
• Salvaging $800M – The Torcetrapib Story
Selective Estrogen Receptor
Modulators (SERM)
• One of the largest
classes of drugs
• Breast cancer,
osteoporosis, birth
control etc.
• Amine and benzine
moiety
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
Adverse Effects of SERMs
cardiac abnormalities
thromboembolic
disorders
loss of calcium
homeostatis
?????
ocular toxicities
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 3(11) e217
0.02
Density
0.04
0.06
Ligand Binding Site Similarity
Search On a Proteome Scale
0.00
SERCA
ERa
0
20
40
Score
60
80
• Searching human proteins covering ~38% of the
drugable genome against SERM binding site
• Matching Sacroplasmic Reticulum (SR) Ca2+ ion
channel ATPase (SERCA) TG1 inhibitor site
• ERa ranked top with p-value<0.0001 from reversed
search against SERCA
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 3(11) e217
Structure and Function of SERCA
• Regulating cytosolic
calcium levels in
cardiac and skeletal
muscle
• Cytosolic and
transmembrane
domains
• Predicted SERM
binding site locates in
the TM, inhibiting Ca2+
uptake
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 3(11) e217
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
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 3(11) e217
Agenda
• Computational Methodology
• Repositioning an Existing Drug - The TB
Story
• Side Effects - The Tamoxifen Story
• Salvaging $800M – The Torcetrapib Story
The Torcetrapib Story
Cholesteryl Ester Transfer Protein (CETP)
CETP inhibitor
X
CETP
LDL
Bad Cholesterol
HDL
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 trocetrapib with the docking score around -8.0.
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
The Torcetrapib Story
/-4.43
/-5.63
/-7.08
/-0.58
/-7.09
/-9.42
/ -5.95
-8.617 / -6.17
???
??? (MYR) -4.16
EP distributions in binding
pockets
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
The Torcetrapib Story
/-4.43
/-5.63
/-7.08
/-0.58
/-7.09
/-9.42
/ -5.95
-8.617 / -6.17
???
??? (MYR) -4.16
JTT705
Torcetrapib
Anacetrapib
JTT705
VDR
–
RAS
+
RXR
PPARα
PPARδ
FA
?
FABP
?
?
PPARγ
High blood
pressure
+
Anti-inflammatory
function
JNK/IKK pathway
JNK/NF-KB pathway
Immune response
to infection
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
The Torcetrapib Story
/-4.43
/-5.63
/-7.08
/-0.58
/-7.09
/-9.42
/ -5.95
-8.617 / -6.17
???
??? (MYR) -4.16
JTT705
Torcetrapib
Anacetrapib
JTT705
VDR
–
RAS
+
RXR
PPARα
PPARδ
FA
?
FABP
?
?
PPARγ
High blood
pressure
+
Anti-inflammatory
function
JNK/IKK pathway
JNK/NF-KB pathway
Immune response
to infection
The Torcetrapib Story
Summary
• We have established a protocol to look
for off-targets for existing therapeutics
and NCEs
• Understanding these in the context of
pathways would seem to be the next
step towards a new understanding
• Lots of other opportunities to examine
existing drugs
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
Summary
Estimated Capitalized Costs for New Chemical Entities
(NCEs) Entering Each Phase
• Estimated costs for
a drug withdrawal:
~ 60.0 millions
• Phase III is most
costly: fail fast, fail
cheap
M. Dickson & J. P. Gagnon, Nature Review Drug Discovery 3(2004) p417-429
Acknowledgements
Lei Xie
Li Xie
Jian Wang
Sarah Kinnings
Nancy Buchmeier
Support Open Access
www.pdb.org • info@rcsb.org
Implications on Drug Development
Affinity (ER Site)
Affinity (SERCA)
Affinity Difference
Bazedoxifene(BAZ)
-9.44 +/- 0.54
-7.23 +/- 0.13
2.21
Lasofoxifene(LAS)
-8.66 +/- 0.40
-6.54 +/- 0.20
2.12
Ormeloxifene(ORM)
-8.67 +/- 0.18
-5.84 +/- 0.33
2.83
Raloxifene(RAL)
-8.08 +/- 0.64
-5.78 +/- 0.23
2.30
4-hydroxytamoxifen(OHT)
-7.67 +/- 0.47
-5.40 +/- 0.15
2.27
Tamoxifen(TAM)
-7.30 +/- 0.28
-5.64 +/- 0.28
1.66
• Taking account of both target and off-target for
lead optimization
• Drug delivery and administration regime
Swiss-Prot - 20 Year Celebration
Northwestern
Jan
55
Improved Performance of Alignment Quality
and Search Sensitivity and Specificity
90
0.03
Amino Acid Grouping
Chemical Similarity
Substitution Matrix
Profile-Profile
80
Amino Acid Group
Chemical Similarity
Substitution Matrix
Profile-Profile
0.025
70
False Positive Ratio
Frequency (%)
60
50
40
30
0.02
0.015
0.01
20
0.005
10
0
0
<1.0
<3.0
<5.0
<7.0
<9.0
<11.0
RMSD (Angsgroms)
.
RMSD distribution of the aligned common fragments of ligands from
247 test cases showing four scores: amino acid grouping, chemical
similarity, substitution matrix and profile-profile.
0
0.04
0.08
0.12
True Positive Ratio
0.16
0.2
2D small molecule similarity
between existing and potential
ENR inhibitors
Entacapone
Tolcapone
2D Similarity to Tolcapone
15
2D Similarity to Entacapone
6
0
2
4
Density
Density
5
AYM
p=0.065
0
Density
Density
8
10
10
12
ZAM
p=0.205
0.00
0.05
0.10
0.15
0.20
0.25
Tanimoto Coefficient
Tanimoto
Coefficient
0.30
0.00
0.05
0.10
0.15
0.20
0.25
Tanimoto Coefficient
Tanimoto
Coefficient
0.30
0.35
Docking existing and potential InhA
inhibitors onto COMT and InhA
InhA inhibitor
Docking score Docking score
with InhA
with COMT
468
-6.57 +/- 1.27
-4.42
566
-6.24 +/- 0.92
-3.96
641
-6.00 +/- 1.51
-5.92
665
-5.18 +/- 0.72
-4.20
744
-6.07 +/- 1.28
-5.47
5PP
-5.99 +/- 0.48
-3.90
8PS
-6.51 +/- 0.95
-4.04
GEQ
-6.29 +/- 1.61
-4.45
Triclosan
-6.34 +/- 0.68
-4.05
Entacapone
-4.91 +/- 0.97
-4.49
Entacapone (N5)
-5.25 +/- 0.93
-4.02
Entacapone (N6)
-5.10 +/- 1.03
-3.81
Tolcapone
-5.85 +/- 0.74
-4.68
Correlation of binding affinity
profiles between COMT and InhA
• Tolcapone-like molecules
3
2
1
0
Control Docking Score
-1
-2
-3
0
2
4
6
Control Docking Score
Tolcapone-like molecules (R=0.35)
-2
ENR Docking Score
InhA Docking Score
Tolcapone-like molecules (R=0.36)
-2
0
2
4
COMT Docking Score
COMT Docking Score
6
-2
0
2
4
6
COMT Docking Score
COMT Docking Score
Binding pose analysis of potential
InhA inhibitors with InhA
Asp110
Asp115
15.25Å
14.53Å
Glu210
11.54Å
Comparison of surface
electrostatic potential between
COMT and InhA functional sites
• Electrostatic potentials of COMT and
InhA calculated using APBS
• Predicted binding poses of entacapone
and tolcapone inserted into proteins
• Qualitative similarities between COMT
and InhA functional sites observed
• In both cases, nitrite groups of
entacapone and tolcapone associated
with positively charged region of active
site
Tolcapone
Entacapone
Comparison of surface electrostatic
potential between COMT and InhA
functional
sites
COMT
InhA
Advantage to Using Ligand Site Similarity
Small molecule
Similarity
Protein
Sequence/Structure
Similarity
Protein
Functional Site
Similarity
• Poor correlation
between structure and
activity
• Infinite chemical space
. Not adequately reflecting
functional relationship
. Not directly addressing
drug design problem
. Build closer structurefunction relationships
. Limit chemical space
through co-evolution
Correlation of Binding Affinity
Profiles between COMT and ENR
• Entacapone-like molecules
Entacapone-like molecules (R=0.38)
2
1
0
-1
-2
-3
Control Docking Score
Control Docking Score
6
4
2
0
ENR Docking Score
ENR Docking Score
8
3
Entacapone-like molecules (R=0.42)
0
2
4
6
COMT Docking Score
COMT Docking Score
Linear regression
Repositioning an Existing Drug - The TB Story
0
2
4
COMT Docking Score
COMT Docking Score
2 identical sites
6