High-throughput Computational Strategies for Proteomics

Philip E. BourneUniversity of California San Diego

pbourne@ucsd.eduhttp://www.sdsc.edu/pb

PepTalk – January 13, 2011

As Applied to Drug Discovery

High-throughput Computation Can Be Applied on Three Axes

Target

Disease

Drug

Cheminfomatics

HTS

Docking

One to Multiple TargetsBioinformatics

Associative Transfer of Indications

Here I will focus mostly on the notion of multiple targets

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

A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690

Multiple Drugs Multiple Targets

• Gene knockouts only effect phenotype in 10-20% of cases , why? – redundant functions – alternative network routes – robustness of interaction networks

• 35% of biologically active compounds bind to more than one target

Paolini et al. Nat. Biotechnol. 2006 24:805–815

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

7

PKA

Phosphoinositide-3 Kinase Phosphoinositide-3 Kinase (D) and Actin-Fragmin (D) and Actin-Fragmin Kinase (E)Kinase (E)

ChaK (“Channel Kinase”)ChaK (“Channel Kinase”)

Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49.

8

Can We Propose an Evolutionary History for the Protein Kinase-Like Superfamily? •Bayesian inference of phylogeny (MrBayes)

•Manual structure alignment produces very high-quality sequence alignment of diverse homologues

•But, sequence information too degraded to produce branching with sufficient support (i.e. a high posterior probability)

•Addition of a matrix of structural characteristics (similar to morphological characteristics) produces a well supported combined model

•Neither sequence structural characteristics sufficient to alone produce resolved tree, must be used in combination.

1BO1 Atypical 0 0 0 0 1

1IA9 Atypical 1 1 1 1 0

1E8X Atypical 1 0 1 1 1

1CJA Atypical 1 0 1 1 1

1NW1 Atypical 1 0 1 0 0

1J7U Atypical 1 0 1 0 1

1CDK AGC 1 1 1 0 1

1O6L AGC 1 1 1 0 1

1OMW AGC 1 1 1 0 1

1H1W AGC 1 1 1 0 1

1MUO Other 1 1 1 0 1

1TKI CAMK 1 0 1 0 1

1JKL CAMK 1 0 1 0 1

1A06 CAMK 1 0 1 0 1

1PHK CAMK 1 0 1 0 1

1KWP CAMK 1 0 1 0 1

1IA8 CAMK 1 0 1 0 0

1GNG CMGC 1 0 1 0 1

1HCK CMGC 1 0 1 0 1

1JNK CMGC 1 0 1 0 1

1HOW CMGC 1 0 1 0 1

1LP4 Other 1 0 1 0 1

1F3M STE 1 0 1 0 1

1O6Y Other 1 0 1 0 1

1CSN CK1 1 0 1 0 1

1B6C TKL 1 0 1 0 1

2SRC TK 1 0 1 0 1

1LUF TK 1 0 1 0 1

1IR3 TK 1 0 1 0 1

1M14 TK 1 0 1 0 1

1GJO TK 1 0 1 0 1

Example columns:

1) Ion pair analogous to K72-E91 in PKA

2) α-Helix B present

3) State of α-Helix C (0: kinked, 1: straight)

4) State of Strand 4 (0: kinked, 1: straight)

5) α-Helix D present

1 2 3 4 5

Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49.

9

AFK

PI3K

CK

APH

ChaK

PIPKIIβ

AGC

CAMK

CMGC

CK1 TK

TKL

Proposed Evolutionary History for the Protein Kinase-Like Superfamily

•Atypical kinase families: Blue

•Typical protein kinase groups (subfamilies): Red

•Branch labels: posterior probability of branch

• Suggests distinctive history for atypical kinases, as opposed to intermittent divergence from the typical protein kinases (TPKs)

• TPK portion of tree shows high degree of agreement with Manning tree

• Branching is supported by species representation of kinase families

0.97

1.0

0.78

0.85

0.64

Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49.

What That Study Told Us

• Structure comparison algorithms are still not good enough or comprehensive enough to provide the level of detail we need for large scale studies….

• We are starting to address this through our research and the RCSB PDB

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

This begins to address the issue of multiple targets that share global similarity.. but

often that is not the case .. we need to focus on binding site

similarity

Our Approach

• We can characterize a known protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale independent of global structure similarity

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

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. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)

4. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)

5. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)

6. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)

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

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

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

The Future as a High Throughput Approach…..

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

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. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)

4. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)

5. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)

6. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (under review)

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• 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

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

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

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

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

The Future as a Dynamical Network Approach

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

Acknowledgements

Sarah Kinnings

Lei Xie

Li Xie

http://funsite.sdsc.eduhttp://www.slideshare.net/pebourne/ucl120810

Roger ChangBernhard Palsson