network pharmacology tri-con 022212

Computational Approaches in Network Pharmacology

Philip E. BourneUniversity of California San Diego

[email protected]://www.sdsc.edu/pb

Tri-Con San Francisco, Feb. 22, 2012

mailto:[email protected]

http://www.sdsc.edu/pb

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

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


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


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


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

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

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%


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


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


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

http://funsite.sdsc.edu/

network pharmacology tri-con 022212

Education

drug binding sites

drug molecule

implications of drug

drug information

geometric potential

drug resources fda

nonbinding sites

peter rose drug