Transcript Protein Data Bank Advisory Committee

Off-Targets
Philip E. Bourne
University of California San Diego
[email protected]
http://www.sdsc.edu/pb/edu/biom230/off-targets.ppt
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Motivation
When you add a foreign chemical into
something as complex as a human
being do you really believe that drug is
binding to only a single receptor?
The Drug Discovery Pipeline
Very
Selective
Collective
Effect
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 - Hence fail early and cheaply
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
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 - 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 

neighbors
Pi
cos(ai)  1.0

Di  1.0
2.0
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
Nothing in Biology {including Drug
Discovery} Makes Sense
Except in the Light of Evolution
Theodosius Dobzhansky (1900-1975)
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
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
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
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
• 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 ...
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
• At least some in vivo support
• Assay of direct binding of entacapone and
tolcapone to InhA 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
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
• 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
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
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
/-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
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
Acknowledgements
Lei Xie
Li Xie
Jian Wang
Sarah Kinnings
Nancy Buchmeier
Support Open Access