A Systematic Approach to Identifying Protein-Ligand Binding Profiles on a Proteome Scale and its Application to Pharmaceutical Sciences Philip E. Bourne University of California San Diego Support Open Access AFP 2008 What I Will Cover Today While we are interested in automated function prediction, we are particularly interested in how AFP techniques can be applied to the pharmaceutical sciences this is what I will cover today proteome.sdsc.edu More Specifically If we can predict where a ligand might bind to a receptor protein not only have we learnt something about the function of that protein, if that ligand happens to have therapeutic properties we might learn something about the primary and secondary targets of that compound The truth is we know very little about how the major drugs we take work We know even less about what side effects they might have Drug discovery seems to be approached in a very consistent and conventional way The cost of bringing a drug to market is huge ~$800M The cost of failure is even higher e.g. Vioxx - $4.85Bn - Hence fail early and cheaply Why Bother? Nothing in Biology {Including Functional Prediction & Hence Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky ( ) Our Approach What Has Evolution Taught Us? Global 3D similarity and sequence similarity do not tell the whole story Perhaps a ligand binding site is what has passed from generation to generation while virtually all other aspects of the protein have changed? What Has Evolution Taught Us About Drug Discovery? If that were true and evolutionarily related ligand binding sites could be found, they presumably would exist across very diverse gene families From the perspective of drug discovery such sites would have significant implications What if We can characterize a protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale? We could perhaps find alternative binding sites (off-targets) for existing pharmaceuticals? We could use it for lead optimization and possible ADME/Tox prediction What Do Off-targets Tell Us? One of three things: 1.Nothing 2.A possible explanation for a side-effect of a drug 3.A possible repositioning of a drug to treat a completely different condition Today I will give you examples of both 2 and 3 and illustrate the complexity of the problem Agenda Computational Methodology Side Effects - The Tamoxifen Story Repositioning an Existing Drug - The TB Story Salvaging $800M The Torcetrapib Story Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Generic NameOther NameTreatmentPDBid LipitorAtorvastatinHigh cholesterol1HWK, 1HW8 Testosterone Osteoporosis1AFS, 1I9J.. TaxolPaclitaxelCancer1JFF, 2HXF, 2HXH ViagraSildenafil citrateED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. DigoxinLanoxinCongestive 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 Characterization of the Ligand Binding Site - Conceptual 1.Represent the protein structure 2.Determine the environmental boundary 3.Determine the protein boundary 4.Computation of the geometric potential 5.Computation of the virtual ligand Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9Computational Methodology 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):S9Computational Methodology Discrimination Power of the Geometric Potential Geometric potential can distinguish binding and non-binding sites 1000 Geometric Potential Scale Computational MethodologyXie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9 Boundary Accuracy of Ligand Binding Site Prediction ~90% of the binding sites can be identified with above 50% sensitivity The specificity of ~70% binding sites identified is above 90% Computational Methodology So Far Geometric potential dependant on local environment of a residue relative to other residues and the environmental boundary Geometric potential reasonably good at discriminating between ligand binding sites and non-ligand binding sites Boundary of the binding site reasonably well defined How to compare sites ??? Computational Methodology Geometric and graph characterization of the protein structure Chemical similarity matrix and evolutionary relationship with profile-profile comparison Optimum alignment with maximum-weight sub- graph algorithm Identification of Functional Similarity with Local Sequence Order Independent Alignment 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 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 AStructure 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 So What is the Potential of this Methodology? Lead Discovery from Fragment Assembly Privileged molecular moieties in medicinal chemistry Structural genomics and high throughput screening generate a large number of protein- fragment complexes Similar sub-site detection enhances the application of fragment assembly strategies in drug discovery 1HQC: Holliday junction migration motor protein from Thermus thermophilus 1ZEF: Rio1 atypical serine protein kinase from A. fulgidus Lead Optimization from Conformational Constraints Same ligand can bind to different proteins, but with different conformations By recognizing the conformational changes in the binding site, it is possible to improve the binding specificity with conformational constraints placed on the ligand 1ECJ: amido-phosphoribosyltransferase from E. Coli 1H3D: ATP-phosphoribosyltransferase from E. Coli Finding Secondary Binding Sites for Major Pharmaceuticals Scan known binding sites for major pharmaceuticals bound to their receptors against the human and other druggable proteomes Filter! Try and correlate strong hits with known data from the literature, databases, clinical trials etc. to provide molecular evidence of secondary effects 43,738 Human Proteins 3,158 Human Proteins (10,730 PDB Structures) 13,865 Human Proteins (2,002 Drug Targets) 1,585 PDB Structures (929 Drug Targets) remove redundant structures with 30% sequence identity, map human proteins to PDB structures with >95% sequence identity map human proteins to drug targets with BLAST e-value < map drug targets to PDB structures cover 929/2,002 = 46.4% drug targets structurally remove redundant structures with 30% sequence identity 2,586 PDB Structures 825 PDB Structures (druggable) Agenda Computational Methodology Repositioning an Existing Drug - The TB Story Side Effects - The Tamoxifen Story Salvaging $800M The Torcetrapib Story Tuberculosis (TB) One third of global population infected Kills 2 million people each 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 an Existing Drug - The TB Story Hypothesis Drawn from the Study of Evolution We were looking for connections (evolutionary linkages) across fold and functional space through an all- by-all comparison of ligand binding sites Repositioning an Existing Drug - The TB Story Found.. Evolutionary linkage between: NAD-binding Rossmann fold S-adenosylmethionine (SAM)-binding domain of SAM- dependent methyltransferases Catechol-O-methyl transferase (COMT) is SAM- dependent methyltransferase Entacapone and tolcapone are used as COMT inhibitors in Parkinsons disease treatment Hypothesis: Further investigation of NAD-binding proteins may uncover a potential new drug target for entacapone and tolcapone Repositioning an Existing Drug - The TB Story Functional Site Similarity between COMT and ENR Entacapone and tolcapone docked onto 215 NAD- binding proteins from different species M.tuberculosis Enoyl-acyl carrier protein reductase ENR (InhA) discovered as potential new drug target ENR is the primary target of many existing anti-TB drugs but all are very toxic ENR catalyses the final, rate-determining step in the fatty acid elongation cycle Alignment of the COMT and ENR binding sites revealed similarities... Repositioning an Existing Drug - The TB Story Binding Site Similarity between COMT and ENR COMT SAM (cofactor) BIE (inhibitor) NAD (cofactor) ENR 641 (inhibitor) Repositioning an Existing Drug - The TB Story 2D Small Molecule (Dis)-Similarity Between Existing and Potential ENR Inhibitors Entacapone and tolcapone molecules compared to 22 existing ENR inhibitors Background distributions created from a random sample of 15,000 drug-like molecules P-values calculated not significant Unlikely that conventional screening methods would identify entacapone and tolcapone as potential ENR inhibitors Repositioning an Existing Drug - The TB Story Docked Existing and Potential ENR inhibitors onto COMT and ENR Entacapone, tolcapone and existing ENR inhibitors docked onto ENR and COMT, respectively Predicted binding affinities of entacapone and tolcapone towards ENR fell within the range of binding affinities exhibited by known ENR inhibitors Cross-reactivity between ENR and COMT demonstrated Repositioning an Existing Drug - The TB Story In Vivo Studies Quantitative and microplate assays of Mtb agree Entacapone - 80% growth inhibition with 62 ug/ml; 100% inhibition with 2x the dose Tolcapone similar results Courtesy Nancy Buchmeier Repositioning an Existing Drug - The TB Story Summary of the TB Story 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 At least some in vivo support Assay of direct binding of entacapone and tolcapone to ENR under way Repositioning an Existing Drug - The TB Story Agenda Computational Methodology Repositioning an Existing Drug - The TB Story Side Effects - The Tamoxifen Story Salvaging $800M The Torcetrapib Story 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 StoryPLoS Comp. Biol., (11) e217 Adverse Effects of SERMs cardiac abnormalities thromboembolic disorders ocular toxicities loss of calcium homeostatis ????? Side Effects - The Tamoxifen StoryPLoS Comp. Biol., 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< from reversed search against SERCA ER SERCA Side Effects - The Tamoxifen StoryPLoS Comp. Biol., 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 StoryPLoS Comp. Biol., 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 StoryPLoS Comp. Biol., 3(11) e217 Agenda Computational Methodology Repositioning an Existing Drug - The TB Story Side Effects - The Tamoxifen Story Salvaging $800M The Torcetrapib Story The Torcetrapib Story 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)very low densitylow density lipoproteinshigh density lipoproteins 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 trocetrapib with the docking score around HDLLDL CETP CETP inhibitor X Bad CholesterolGood Cholesterol The Torcetrapib Story Off-targetPDB IdsTorcetrapibAnacetrapibJTT705Complex ligand CETP2OBD / / / (PCW) Retinoid X receptor1YOW 1ZDT / / / (POE) PPAR delta1Y0S / / / (331) PPAR alpha2P / / / (735) PPAR gamma1ZEO / -7.31> 0.0 / / (C01) Vitamin D receptor1IE8>0.0/ -4.73>0.0 / / (KH1) Glucocorticoid Receptor 1NHZ 1P93 /-4.43 /-5.63 /-7.08 /-0.58 /-7.09 /-9.42 Fatty acid binding protein 2F73 2PY1 2NNQ >0.0/ >0.0/-6.13 /-6.40 >0.0/ >0.0/ / / /-6.33 /6.35 ??? T-Cell CD1B1GZP / / / (GM2) IL-10 receptor1LQS / / / -5.95??? GM-2 activator2AG / / / -6.17??? (MYR) (3CA2+) CARDIAC TROPONIN C 1DTL /-5.83 /-6.71 /-5.79 cytochrome bc1 complex 1PP9 (PEG) /-6.97 /-9.07 / PP9 (HEM) /-7.21 /8.79 /-8.94 human cytoglobin1V5H /-4.89 /-7.00 /-4.94 The Torcetrapib Story Docking Scores eHits/Autodock EP distributions in binding pockets The Torcetrapib Story Off-targetPDB IdsTorcetrapibAnacetrapibJTT705Complex ligand CETP2OBD / / / (PCW) Retinoid X receptor1YOW 1ZDT / / / (POE) PPAR delta1Y0S / / / (331) PPAR alpha2P / / / (735) PPAR gamma1ZEO / -7.31> 0.0 / / (C01) Vitamin D receptor1IE8>0.0/ -4.73>0.0 / / (KH1) Glucocorticoid Receptor 1NHZ 1P93 /-4.43 /-5.63 /-7.08 /-0.58 /-7.09 /-9.42 Fatty acid binding protein 2F73 2PY1 2NNQ >0.0/ >0.0/-6.13 /-6.40 >0.0/ >0.0/ / / /-6.33 /6.35 ??? T-Cell CD1B1GZP / / / (GM2) IL-10 receptor1LQS / / / -5.95??? GM-2 activator2AG / / / -6.17??? (MYR) (3CA2+) CARDIAC TROPONIN C 1DTL /-5.83 /-6.71 /-5.79 cytochrome bc1 complex 1PP9 (PEG) /-6.97 /-9.07 / PP9 (HEM) /-7.21 /8.79 /-8.94 human cytoglobin1V5H /-4.89 /-7.00 /-4.94 The Torcetrapib Story Docking Scores eHits/Autodock RAS PPAR RXR VDR + High blood pressure FABP FA + Anti-inflammatory function ? Torcetrapib Anacetrapib JTT705 JNK/IKK pathway JNK/NF-KB pathway ? Immune response to infection JTT705 PPAR PPAR ? The Torcetrapib Story Off-targetPDB IdsTorcetrapibAnacetrapibJTT705Complex ligand CETP2OBD / / / (PCW) Retinoid X receptor1YOW 1ZDT / / / (POE) PPAR delta1Y0S / / / (331) PPAR alpha2P / / / (735) PPAR gamma1ZEO / -7.31> 0.0 / / (C01) Vitamin D receptor1IE8>0.0/ -4.73>0.0 / / (KH1) Glucocorticoid Receptor 1NHZ 1P93 /-4.43 /-5.63 /-7.08 /-0.58 /-7.09 /-9.42 Fatty acid binding protein 2F73 2PY1 2NNQ >0.0/ >0.0/-6.13 /-6.40 >0.0/ >0.0/ / / /-6.33 /6.35 ??? T-Cell CD1B1GZP / / / (GM2) IL-10 receptor1LQS / / / -5.95??? GM-2 activator2AG / / / -6.17??? (MYR) (3CA2+) CARDIAC TROPONIN C 1DTL /-5.83 /-6.71 /-5.79 cytochrome bc1 complex 1PP9 (PEG) /-6.97 /-9.07 / PP9 (HEM) /-7.21 /8.79 /-8.94 human cytoglobin1V5H /-4.89 /-7.00 /-4.94 The Torcetrapib Story Docking Scores eHits/Autodock RAS PPAR RXR VDR + High blood pressure FABP FA + Anti-inflammatory function ? Torcetrapib Anacetrapib JTT705 JNK/IKK pathway JNK/NF-KB pathway ? Immune response to infection JTT705 PPAR PPAR ? The Torcetrapib Story Summary We have established a protocol to look for off-targets for existing therapeutics and NCEs Understanding these in the context of pathways would seem to be the next step towards a new understanding Lots of other opportunities to examine existing drugs Bioinformatics Final Examples.. Donepezil for treating Alzheimers shows positive effects against other neurological disorders Orlistat used to treat obesity has proven effective against certain cancer types Ritonavir used to treat AIDS effective against TB Nelfinavir used to treat AIDS effective against different types of cancers M. Dickson & J. P. Gagnon, Nature Review Drug Discovery 3(2004) p Summary Estimated Capitalized Costs for New Chemical Entities (NCEs) Entering Each Phase Estimated costs for a drug withdrawal: ~ 60.0 millions Phase III is most costly: fail fast, fail cheap Acknowledgements Support Open Access Sarah Kinnings Nancy Buchmeier Lei Xie Li Xie Jian Wang Swiss-Prot - 20 Year CelebrationNorthwestern Jan Implications on Drug Development Affinity (ER Site)Affinity (SERCA)Affinity Difference Bazedoxifene(BAZ) / / Lasofoxifene(LAS) / / Ormeloxifene(ORM) / / Raloxifene(RAL) / / hydroxytamoxifen(OHT) / / Tamoxifen(TAM) / / Taking account of both target and off-target for lead optimization Drug delivery and administration regime Improved Performance of Alignment Quality and Search Sensitivity and Specificity RMSD distribution of the aligned common fragments of ligands from 247 test cases showing four scores: amino acid grouping, chemical similarity, substitution matrix and profile-profile.. 2D small molecule similarity between existing and potential ENR inhibitors AYM p=0.065 ZAM p=0.205 Tanimoto Coefficient Density EntacaponeTolcapone Docking existing and potential InhA inhibitors onto COMT and InhA Correlation of binding affinity profiles between COMT and InhA Tolcapone-like molecules COMT Docking Score InhA Docking Score COMT Docking Score Control Docking Score Binding pose analysis of potential InhA inhibitors with InhA Asp110 Glu210 Asp Comparison of surface electrostatic potential between COMT and InhA functional sites Electrostatic potentials of COMT and InhA calculated using APBS Predicted binding poses of entacapone and tolcapone inserted into proteins Qualitative similarities between COMT and InhA functional sites observed In both cases, nitrite groups of entacapone and tolcapone associated with positively charged region of active site Comparison of surface electrostatic potential between COMT and InhA functional sites COMTInhA Entacapone Tolcapone Advantage to Using Ligand Site Similarity Protein Sequence/Structure Similarity Protein Functional Site Similarity Small molecule Similarity. Not adequately reflecting functional relationship. Not directly addressing drug design problem Poor correlation between structure and activity Infinite chemical space. Build closer structure- function relationships. Limit chemical space through co-evolution Correlation of Binding Affinity Profiles between COMT and ENR Entacapone-like molecules COMT Docking Score ENR Docking Score COMT Docking Score Control Docking Score Repositioning an Existing Drug - The TB Story Linear regression2 identical sites