network pharmacology tri-con 022212
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Computational Approaches in Network Pharmacology
Philip E. BourneUniversity of California San Diego
pbourne@ucsd.eduhttp://www.sdsc.edu/pb
Tri-Con San Francisco, Feb. 22, 2012
Big Questions in the Lab1. Can we improve how science is
disseminated and comprehended?
2. What is the ancestry and organization 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?
Motivators
Our Motivation• 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 2 689-700Motivators
Our Broad Approach
• Involves the fields of:– Structural bioinformatics– Cheminformatics – Biophysics– Systems biology – Pharmaceutical chemistry
• L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52: 361-379
• L. Xie, S.L. Kinnings, L. Xie and P.E. Bourne 2012 Predicting the Polypharmacology of Drugs: Identifying New Uses Through Bioinformatics and Cheminformatics Approaches in Drug Repurposing M. Barrett and D. Frail (Eds.) Wiley and Sons. (available upon request)
Disciplines Touched & 2012 Reviews
A Quick Aside – RCSB PDB Pharmacology/Drug View 2012
• 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’s Drug Work RCSB PDB Team
Led by Peter Rose
A Quick Aside PDB Scope/Deliverables
• Part I: small molecule drugs, nutraceuticals, and their targets ( DrugBank) - 2012
• Part II: peptide derived compounds (PRD)- tbd• Part III: toxins and toxin targets (T3DB), human
metabolites (HMDB)• Part IV: biotherapeutics, i.e., monoclonal antibodies• Part V: veterinary drugs (FDA Green Book)
RCSB PDB’s Drug Work
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 a static and dynamic network-based approach to understand the implications of drug binding to multiple sites
Methodology
Applications Thus Far
• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir)
• Early detection of side-effects (J&J)• Late detection of side-effects (torcetrapib)• Lead optimization (e.g., SERMs, Optima,
Limerick)• Drugomes (TB, P. falciparum, T. cruzi)
Applications
Approach - Need to Start with a 3D Drug-Receptor Complex – Either Experimental or
Modeled
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
Some Numbers to Show Limitations
TB-drugome pF-DrugomeTarget gene 3996 5491Target protein in PDB 284 136Solved structure in PDB 749 333Reliable homology models 1446 1236Structure coverage 43.29% 25.02%Drugs 274 321Drug binding sites 962 1569
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
Applications Thus Far
• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir)
• Early detection of side-effects (J&J)• Late detection of side-effects (torcetrapib)• Lead optimization (e.g., SERMs, Optima,
Limerick)• Drugomes (TB, P. falciparum, T. cruzi)
Applications
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 7(4) e1002037
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 2011 7(4) e1002037
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 2011 7(4) e1002037
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 RepositioningPLoS Comp. Biol., 2011 7(4) e1002037
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 RepositioningPLoS Comp. Biol., 2011 2011 7(4) e1002037
Applications Thus Far
• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir)
• Early detection of side-effects (J&J)• Late detection of side-effects (torcetrapib)• Lead optimization (e.g., SERMs, Optima,
Limerick)• Drugomes (TB, P. falciparum, T. cruzi)
Applications
Case Study: Torcetrapib Side Effect
• Cholesteryl ester transfer protein (CETP) inhibitors treat cardiovascular disease by raising HDL and lowering LDL cholesterol (Torcetrapib, Anacetrapib, JTT-705).
• Torcetrapib withdrawn due to occasional lethal side effect, severe hypertension.
• Cause of hypertension undetermined; off-target effects suggested.
• Predicted off-targets include metabolic enzymes. Renal function is strong determinant of blood pressure. Causal off-targets may be found through modeling kidney metabolism.
Constraint-based Metabolic Modeling
S · v = 0
Matrix representation of network
Metabolic network reactions Flux space
Change in system capacity
Perturbation constraint
HEX1 ?
PGI ?
PFK ?
FBA ?
TPI ?
GAPD ?
PGK ?
PGM ?
ENO ?
PYK ?
Steady-state assumption
Flux
Recon1: A Human Metabolic Network
(Duarte et al Proc Natl Acad Sci USA 2007)http://bigg.ucsd.edu
Global Metabolic MapComprehensively represents known reactions in human cells
Pathways (98)
Reactions (3,311)
Compounds (2,712)
Genes (1,496)Transcripts (1,905)
Proteins (2,004)
Compartments (7)
Context-specific Modeling Pipeline
metabolic network
metabolomic biofluid & tissue localization data
constrain exchange
fluxespreliminary
model
gene expression
data
refine based on
capabilities
set flux constraints
objective function
literature
GIMME
normalize & set threshold
set minimum objective flux
model
metabolic influx
metabolic efflux
Predicted Hypertension Causal Drug Off-Targets
OfficialSymbol Protein
Off-TargetPrediction
FunctionalSiteOverlap
ReactionsLimited byExpression
ImpactsRenalFunction inSimulation
Stronger Drug Binding Affinity Cryptic Genetic Risk Factors
PTGISProstacyclinsynthase
x x x x x
ACOX1 Acyl CoA oxidase x x x x x
AK3L1 Adenylate kinase 4 x x x x
HAO2 Hydroxyacid oxidase 2 x x x xSLC3A1; SLC7A9; SLC7A10;
ABCC1
MT-COIMitochondrialcytochrome c oxidase I x x x CYP27B1; ABCC1
UQCRC1Ubiquinol-cytochrome creductase core protein I x x x CYP27B1; ABCC1
*Clinically linked to hypertension.
Applications Thus Far
• Repositioning existing pharmaceuticals and NCEs (e.g., tolcapone, entacapone, nelfinavir)
• Early detection of side-effects (J&J)• Late detection of side-effects (torcetrapib)• Lead optimization (e.g., SERMs, Optima,
Limerick)• Drugomes (TB, P. falciparum, T. cruzi)
Applications
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
Acknowledgements
Sarah Kinnings
Lei Xie
Li Xie
http://funsite.sdsc.edu
Roger ChangBernhard Palsson
Jian Wang
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