Download - Polypharmacology - NBIC April 20, 2011
Bioinformatics Meets Systems Biology for Early Stage Drug
Discovery
Philip E. BourneUniversity of California San Diego
[email protected]://www.sdsc.edu/pb
NBIC – April 20, 2011
Big Questions in the Lab1. Can we improve how science is
disseminated and comprehended?
2. What is the ancestry of the protein structure universe and what can we learn from it?
3. Are there alternative ways to represent proteins from which we can learn something new?
4. What really happens when we take a drug?
5. Can we contribute to the treatment of neglected {tropical} diseases?
What Really Happens When We Take a Drug?
• If we knew the answer we could:
– Contribute to the design of improved drugs with minimal side effects
– Contribute to how existing drugs and NCEs might be repositioned
Motivation
Why We Think This is Important
• Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized in most cases – witness the recent success of big pharma
• Stated another way – The notion of one drug, one target, to treat one disease is a little naïve in a complex system
Motivation
Polypharmacology - One Drug Binds to Multiple Targets
• Tykerb – Breast cancer
• Gleevac – Leukemia, GI cancers
• Nexavar – Kidney and liver cancer
• Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive
Collins and Workman 2006 Nature Chemical Biology 2 689-700Motivation
We Are Developing a Theoretical Approach to Address This Fundamental Shift in
Thinking
• Involves the fields of:
– Structural bioinformatics– Cheminformatics – Biophysics– Systems-level biology – Pharmaceutical chemistry
Our Approach
Our Approach
• We characterize a known protein-ligand binding site from a 3D structure (primary site) and search for similar sites (secondary sites) on a proteome wide scale independent of global structure similarity
• We try an integrated approach to understand the implications of drug binding to multiple sites
Our Approach
Which Means …
• We could perhaps find alternative binding sites (off-targets) for existing pharmaceuticals and NCEs?
• If we can make this high throughput we could rationally explore a large network of protein-ligands interactions
Our Approach
What Have These Off-targets and Networks Told Us So Far?
Some Examples…1. Nothing2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
Our Stories
Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many
ExamplesGeneric 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
Nu
mb
er o
f re
leas
ed e
ntr
ies
Year:
A Quick Aside – RCSB PDB Pharmacology/Drug View 2010-2011
• Establish linkages to drug resources (FDA, PubChem, DrugBank, ChEBI, BindingDB etc.)
• Create query capabilities for drug information
• Provide superposed views of ligand binding sites
• Analyze and display protein-ligand interactions
Drug Name Asp
Aspirin
Has Bound Drug% Similarity to Drug Molecule 100
Mockups of drug view features
RCSB PDB Ligand View RCSB PDB Team
A Reverse Engineering Approach to Drug Discovery Across Gene FamiliesCharacterize 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 MethodologyXie and Bourne 2009 Bioinformatics 25(12) 305-312
• Initially assign C atom with a value that is the distance to the environmental boundary
• Update the value with those of surrounding C atoms dependent on distances and orientation – atoms within a 10A radius define i
0.2
0.1)cos(
0.1
i
Di
PiPGP
neighbors
Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments
Characterization of the Ligand Binding Site - The Geometric Potential
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9Computational Methodology
Discrimination Power of the Geometric Potential
0
0.5
1
1.5
2
2.5
3
3.5
4
0 11 22 33 44 55 66 77 88 99
Geometric Potential
binding site
non-binding site
• Geometric potential can distinguish binding and non-binding sites
100 0
Geometric Potential Scale
Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
For Residue Clusters
Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm
L E R
V K D L
L E R
V K D L
Structure A Structure B
• 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) 5441Computational Methodology
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
ia
i
ib
ib
i
ia SfSfd
fa, fb are the 20 amino acid target frequencies of profile a and b, respectivelySa, Sb are the PSSM of profile a and b, respectively Computational Methodology Xie and Bourne 2008 PNAS, 105(14) 5441
What Have These Off-targets and Networks Told Us So Far?
Some Examples…1. Nothing2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
Our Stories
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
ocular toxicities
loss of calcium homeostatis
?????
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217
Ligand Binding Site Similarity Search On a Proteome Scale
• 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
• ER ranked top with p-value<0.0001 from reversed search against SERCA
ER
0 20 40 60 80
0.0
00
.02
0.0
40
.06
Score
De
nsi
ty
SERCA
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 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., 2007 3(11) e217
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)
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217
Off-Target of SERMscardiac abnormalities
thromboembolic disorders
ocular toxicities
loss of calcium homeostatis
SERCA !
in vivo and in vitro Studies TAM play roles in regulating calcium uptake activity of cardiac SR TAM reduce intracellular calcium concentration and release in the platelets Cataracts result from TG1 inhibited SERCA up-regulation EDS increases intracellular calcium in lens epithelial cells by inhibiting SERCA
in silico Studies Ligand binding site similarity Binding affinity correlation PLoS Comp. Biol., 2007 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., 2007 3(11) e217
What Have These Off-targets and Networks Told Us So Far?
Some Examples…1. Nothing2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
Our Stories
Nelfinavir
• Nelfinavir may have the most potent antitumor activity of the HIV protease inhibitors
Joell J. Gills et al, Clin Cancer Res, 2007; 13(17) Warren A. Chow et al, The Lancet Oncology, 2009, 10(1)
• Nelfinavir can inhibit receptor tyrosine kinase(s)• Nelfinavir can reduce Akt activation
• Our goal: • to identify off-targets of Nelfinavir in the human
proteome• to construct an off-target binding network • to explain the mechanism of anti-cancer activity
Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 To Appear
Possible Nelfinavir Repositioning
binding site comparison
protein ligand docking
MD simulation & MM/GBSABinding free energy calculation
structural proteome
off-target?
network construction & mapping
drug target
Clinical Outcomes
1OHR
Possible Nelfinavir Repositioning
Binding Site Comparison
• 5,985 structures or models that cover approximately 30% of the human proteome are searched against the HIV protease dimer (PDB id: 1OHR)
• Structures with SMAP p-value less than 1.0e-3 were retained for further investigation
• A total 126 structures have significant p-values < 1.0e-3
Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 To Appear
Enrichment of Protein Kinases in Top Hits
• The top 7 ranked off-targets belong to the same EC family - aspartyl proteases - with HIV protease
• Other off-targets are dominated by protein kinases (51 off-targets) and other ATP or nucleotide binding proteins (17 off-targets)
• 14 out of 18 proteins with SMAP p-values < 1.0e-4 are protein kinases
Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 To Appear
p-value < 1.0e-3
p-value < 1.0e-4
Distribution of Top Hits on the Human Kinome
Manning et al., Science, 2002, V298, 1912
Possible Nelfinavir Repositioning
1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition)2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues
H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamidehydroxy O38
EGFR-DJKCo-crys ligand
EGFR-Nelfinavir
Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides
are comparable
DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
Off-target Interaction Network
Identified off-target
Intermediate protein
Pathway
Cellular effect
Activation
Inhibition
Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 To Appear
Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive
The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activitywere detected by immunoblotting.
The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor
Joell J. Gills et al.Clinic Cancer Research September 2007 13; 5183
Nelfinavir inhibits growth of human melanoma cellsby induction of cell cycle arrest
Nelfinavir induces G1 arrest through inhibitionof CDK2 activity.
Such inhibition is not caused by inhibition of Aktsignaling.
Jiang W el al. Cancer Res. 2007 67(3)
BCR-ABL is a constitutively activated tyrosine kinase that causes chronic myeloid leukemia (CML)Druker, B.J., et al New England Journal of Medicine, 2001. 344(14): p. 1031-1037
Nelfinavir can induce apoptosis in leukemia cells as a single agentBruning, A., et al. , Molecular Cancer, 2010. 9:19
Nelfinavir may inhibit BCR-ABL
Possible Nelfinavir Repositioning
Summary
• The HIV-1 drug Nelfinavir appears to be a broad spectrum low affinity kinase inhibitor
• Most targets are upstream of the PI3K/Akt pathway
• Findings are consistent with the experimental literature
• More direct experiment is needed
Possible Nelfinavir Repositioning PLoS Comp. Biol., 2011 To Appear
What Have These Off-targets and Networks Told Us So Far?
Some Examples…1. Nothing2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
Our Stories
The Future as a High Throughput Approach…..
The Problem with Tuberculosis
• One third of global population infected• 1.7 million deaths per 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 - The TB Story
The TB-Drugome
1. Determine the TB structural proteome
2. Determine all known drug binding sites from the PDB
3. Determine which of the sites found in 2 exist in 1
4. Call the result the TB-drugome
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
1. Determine the TB Structural Proteome
284
1, 446
3, 996 2, 266
TB proteome
homology
models
solve
d
structu
res
• High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
2. Determine all Known Drug Binding Sites in the PDB
• Searched the PDB for protein crystal structures bound with FDA-approved drugs
• 268 drugs bound in a total of 931 binding sites
No. of drug binding sites
MethotrexateChenodiol
AlitretinoinConjugated estrogens
DarunavirAcarbose
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
Map 2 onto 1 – The TB-Drugomehttp://funsite.sdsc.edu/drugome/TB/
Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
From a Drug Repositioning Perspective
• Similarities between drug binding sites and TB proteins are found for 61/268 drugs
• 41 of these drugs could potentially inhibit more than one TB protein
No. of potential TB targets
raloxifenealitretinoin
conjugated estrogens &methotrexate
ritonavir
testosteronelevothyroxine
chenodiol
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
Top 5 Most Highly Connected Drugs
Drug Intended targets Indications No. of connections TB proteins
levothyroxine transthyretin, thyroid hormone receptor α & β-1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin
hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor
14
adenylyl cyclase, argR, bioD, CRP/FNR trans. reg., ethR, glbN, glbO, kasB, lrpA, nusA, prrA, secA1, thyX, trans. reg. protein
alitretinoin retinoic acid receptor RXR-α, β & γ, retinoic acid receptor α, β & γ-1&2, cellular retinoic acid-binding protein 1&2
cutaneous lesions in patients with Kaposi's sarcoma 13
adenylyl cyclase, aroG, bioD, bpoC, CRP/FNR trans. reg., cyp125, embR, glbN, inhA, lppX, nusA, pknE, purN
conjugated estrogens estrogen receptor
menopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure
10
acetylglutamate kinase, adenylyl cyclase, bphD, CRP/FNR trans. reg., cyp121, cysM, inhA, mscL, pknB, sigC
methotrexatedihydrofolate reductase, serum albumin
gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis
10
acetylglutamate kinase, aroF, cmaA2, CRP/FNR trans. reg., cyp121, cyp51, lpd, mmaA4, panC, usp
raloxifeneestrogen receptor, estrogen receptor β
osteoporosis in post-menopausal women 9
adenylyl cyclase, CRP/FNR trans. reg., deoD, inhA, pknB, pknE, Rv1347c, secA1, sigC
Vignette within Vignette
• 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
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 - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
What Have These Off-targets and Networks Told Us So Far?
Some Examples…1. Nothing2. A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
Our Stories
The Future as a Dynamical Network Approach
Drug Failure - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387
Cholesteryl Ester Transfer Protein (CETP)
• 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.
HDLLDL
CETP
CETP inhibitor
X
Bad Cholesterol Good Cholesterol
PLoS Comp Biol 2009 5(5) e1000387Drug Failure - The Torcetrapib Story
Computational Evaluation of Drug Off-Target Effects
Proteome
Drug binding site alignments
SMAP
Predicted drug targets
Drug and endogenous substrate binding site analysis
Competitively inhibitable targets
Inhibition simulations in context-specific model
COBRA Toolbox
Predicted causal targets and genetic risk factors
Metabolicnetwork
Scientificliterature
Tissue and biofluid localization data
Gene expression
data
Physiologicalobjectives
System exchange constraints
Flux states optimizing objective
Physiological context-specific
model
Influx
Efflux
Drug response phenotypes
Dru
g ta
rget
s
Physiologicalobjectives
Causal drug targets
All targets
336 genes1587 reactions
Plos Comp. Biol. 2010 6(9): e1000938
Summary
• Generated a large number of testable hypotheses regarding off-targets of known drugs
• A few have been tested experimentally (e.g., encapalone against InhA)
• A possible methodology for use in early stage drug discovery
Acknowledgements
Sarah Kinnings
Lei Xie
Li Xie
http://funsite.sdsc.edu
Roger ChangBernhard Palsson
Jian Wang