Pep Talk San Diego 011311

High-throughput Computational Strategies for Proteomics Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/ pb PepTalk – January 13, 2011 As Applied to Drug Discovery

High-throughput Computation Can Be Applied on Three Axes One to Multiple Targets Bioinformatics Associative Transfer of Indications Target Disease Drug Cheminfomatics HTS Docking

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

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
A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 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 2 689-700 Motivation

PKA Phosphoinositide-3 Kinase (D) and Actin-Fragmin Kinase (E) ChaK (“Channel Kinase”) Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49.

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.
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. 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

AFK PI3K CK APH ChaK PIPKII β 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
Mockups of drug view features RCSB PDB Ligand View RCSB PDB Team Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100

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…
Nothing
A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
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 Examples Computational Methodology 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 Xie 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
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):S9 Computational Methodology

Discrimination Power of the Geometric Potential
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) 5441 Computational 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
f a , f b are the 20 amino acid target frequencies of profile a and b , respectively S a , S b 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
Determine the TB structural proteome
Determine all known drug binding sites from the PDB
Determine which of the sites found in 2 exist in 1
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
High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%
284 1, 446 3, 996 2, 266 TB proteome homology models solved structures 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 Methotrexate Chenodiol Alitretinoin Conjugated estrogens Darunavir Acarbose A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976

Map 2 onto 1 – The TB-Drugome http://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 raloxifene alitretinoin conjugated estrogens & methotrexate ritonavir testosterone levothyroxine 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 methotrexate dihydrofolate 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 raloxifene estrogen 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…
Nothing
A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol. , 2007 3(11) e217)
The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
How to optimize a NCE ( NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
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

binding site comparison protein ligand docking MD simulation & MM/GBSA Binding 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

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

Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 Manning et al., Science , 2002, V298, 1912 Possible Nelfinavir Repositioning

Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 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 benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir 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

The Future as a Dynamical Network Approach

Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 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 Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drug targets Physiological objectives Causal drug targets All targets

Acknowledgements Sarah Kinnings Lei Xie Li Xie http://funsite.sdsc.edu http://www.slideshare.net/pebourne/ucl120810 Roger Chang Bernhard Palsson

Pep Talk San Diego 011311

by Philip Bourne on Jan 13, 2011

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