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Using Protein Structure to Study Network Pharmacology Hauptman Woodward Institute November 5, 2009

Using Protein Structure to Study Network Pharmacology Hauptman Woodward Institute November 5, 2009. Philip E. Bourne University of California San Diego pbourne@ucsd.edu. Big Questions in the Lab. Can we improve how science is disseminated and comprehended?

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Using Protein Structure to Study Network Pharmacology Hauptman Woodward Institute November 5, 2009

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  1. Using Protein Structure to Study Network PharmacologyHauptman Woodward InstituteNovember 5, 2009 Philip E. Bourne University of California San Diego pbourne@ucsd.edu

  2. Big Questions in the Lab • Can we improve how science is disseminated and comprehended? • What is the ancestry of the protein structure universe and what can we learn from it? • Are there alternative ways to represent proteins from which we can learn something new? • What really happens when we take a drug? August 14, 2009

  3. 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 Motivation

  4. The truth is we know very little about how the major drugs we take work – receptors are unknown We know even less about what side effects they might have - receptors are unknown Drug discovery seems to be approached in a very consistent and conventional way The cost of bringing a drug to market is huge ~$800M – drug reuse is a big business The cost of failure is even higher e.g. Vioxx - $4.85Bn - fail early and cheaply Motivation

  5. Why Don’t we Do Better?A Couple of Observations • 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

  6. Implications • Ehrlich’s philosophy of magic bullets targeting individual chemoreceptors has not been realized • Stated another way – The notion of one drug, one target, one disease is a little naïve in a complex system

  7. How Can we Begin to Address the Problem? • Systematic screening for multiple targets • Integration of knowledge from multiple sources • Analyze the impact on the complete network

  8. 2. What is the ancestry of the protein structure universe? 4. What really happens when we take a drug? Valas, Yang & Bourne 2009 Current Opinions in Structural Biology 19:1-6

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

  10. Implications • The ATP binding cassette is preserved yet the enzyme has evolved to bind a variety of different substrates • The evolutionary history of the protein kinase-like superfamily can be traced with careful analysis • So taking this a step further … The Role of Evolution

  11. What if only the binding pocket was conserved and the global structure of the protein has changed? A drug could potentially bind to distinctly different gene families The Role of Evolution

  12. If this is True 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 and NCEs? • We could use it for lead optimization and possible ADME/Tox prediction • We might be able to construct a site similarity network for a given proteome to define multiple targets for dirty drugs The Role of Evolution

  13. What Do Off-targets Tell Us? One of four things: Nothing A possible explanation for a side-effect of a drug A possible repositioning of a drug to treat a completely different condition A multi-target strategy to attack a pathogen Today I will give you examples of 2, 3 and 4 while illustrating the complexity of the problem The Role of Evolution

  14. Agenda Computational Methodology Side Effects - The Tamoxifen Story Repositioning an Existing Drug - The TB Story Salvaging $800M – The Torcetrapib Story The Future? - The TB Drugome

  15. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology

  16. 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

  17. 43,738 Human Proteins map human proteins to drug targets with BLAST e-value < 0.001 map human proteins to PDB structures with >95% sequence identity 13,865 Human Proteins (2,002 Drug Targets) 3,158 Human Proteins (10,730 PDB Structures) remove redundant structures with 30% sequence identity map drug targets to PDB structures 1,585 PDB Structures (929 Drug Targets) 2,586 PDB Structures remove redundant structures with 30% sequence identity, cover 929/2,002 = 46.4% drug targets structurally 825 PDB Structures (druggable) What we Search Against The Human Target List Computational Methodology

  18. Characterization of the Ligand Binding Site - The Geometric Potential • Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments • Initially assign Ca atom with a value that is the distance to the environmental boundary • Update the value with those of surrounding Ca atoms dependent on distances and orientation – atoms within a 10A radius define i Computational Methodology Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

  19. 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

  20. Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm Structure A Structure B L E R V K D L L E R V K D L • 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

  21. 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 fa, fb are the 20 amino acid target frequencies of profile a and b, respectively Sa, Sb are the PSSM of profile a and b, respectively Xie and Bourne 2008 PNAS, 105(14) 5441 Computational Methodology

  22. Nothing in Biology {Including Drug Discovery} Makes Sense Except in the Light of Evolution Theodosius Dobzhansky (1900-1975)

  23. 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 Computational Methodology

  24. 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 Computational Methodology

  25. ScoringThe Point is this Approach Can Now be Applied on a Proteome-wide Scale • Scores for binding site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version • Blosum45 and • b) McLachlan substitution matrices. Xie, Xie and Bourne 2009 Bioinformatics 25(12) 305-312 Computational Methodology

  26. Agenda Computational Methodology Side Effects - The Tamoxifen Story Repositioning an Existing Drug - The TB Story Salvaging $800M – The Torcetrapib Story The Future? - The TB Drugome

  27. 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 Parkinson’s 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

  28. Functional Site Similarity between COMT and InhA Entacapone and tolcapone docked onto 215 NAD-binding proteins from different species M.tuberculosisEnoyl-acyl carrier protein reductaseENR (InhA) discovered as potential new drug target InhA is the primary target of many existing anti-TB drugs but all are very toxic InhA catalyses the final, rate-determining step in the fatty acid elongation cycle Alignment of the COMT and InhA binding sites revealed similarities ... Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Repositioning an Existing Drug- The TB Story

  29. Binding Site Similarity between COMT and InhA COMT SAM (cofactor) BIE (inhibitor) InhA NAD (cofactor) 641 (inhibitor) Repositioning an Existing Drug- The TB Story

  30. 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 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 Repositioning an Existing Drug- The TB Story

  31. 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 an Existing Drug- The TB Story

  32. Agenda Computational Methodology Side Effects - The Tamoxifen Story Repositioning an Existing Drug - The TB Story Salvaging $800M – The Torcetrapib Story The Future? - The TB Drugome

  33. Selective Estrogen Receptor Modulators (SERM) • One of the largest classes of drugs • Breast cancer, osteoporosis, birth control etc. • Amine and benzine moiety PLoS Comp. Biol., 2007 3(11) e217 Side Effects- The Tamoxifen Story

  34. Adverse Effects of SERMs cardiac abnormalities loss of calcium homeostatis thromboembolic disorders ????? ocular toxicities PLoS Comp. Biol., 3(11) e217 Side Effects- The Tamoxifen Story

  35. Structure and Function of SERCASacroplasmic Reticulum (SR) Ca2+ ion channel ATPase • Regulating cytosolic calcium levels in cardiac and skeletal muscle • Cytosolic and transmembrane domains • Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptake PLoS Comp. Biol., 3(11) e217 Side Effects- The Tamoxifen Story

  36. Binding Poses of SERMs in SERCA from Docking Studies • Salt bridge interaction between amine group and GLU • Aromatic interactions for both N-, and C-moiety 6 SERMS A-F (red) PLoS Comp. Biol., 3(11) e217 Side Effects- The Tamoxifen Story

  37. 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 PLoS Comp. Biol., 3(11) e217 Side Effects- The Tamoxifen Story

  38. Agenda Computational Methodology Side Effects - The Tamoxifen Story Repositioning an Existing Drug - The TB Story Salvaging $800M – The Torcetrapib Story The Future? - The TB Drugome

  39. PLoS Comp Biol 2009 5(5) e1000387 The Torcetrapib Story

  40. 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) 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 torcetrapib with the docking score around -8.0. CETP inhibitor X CETP LDL HDL Bad Cholesterol Good Cholesterol The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

  41. Docking Scores eHits/Autodock The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387

  42. JTT705 Torcetrapib Anacetrapib JTT705 VDR – RXR FA + RAS FABP ? PPARα PPARδ ? ? PPARγ High blood pressure + JNK/IKK pathway JNK/NF-KB pathway Anti-inflammatory function PLoS Comp Biol 2009 5(5) e1000387 Immune response to infection The Torcetrapib Story

  43. Agenda Computational Methodology Side Effects - The Tamoxifen Story Repositioning an Existing Drug - The TB Story Salvaging $800M – The Torcetrapib Story The Future? - The TB Drugome

  44. Existing Drugs 3. Protein-ligand Docking … Protein-drug Interactome Structural Proteome 2. Binding site Similarity Drug resistance mechanism New therapeutics Drugome Target identification 1. Structural Determination & Modeling 4.2 Network Integration Drug repurposing Metabolome Side effect prediction Genome 4.1 Network Reconstruction Bioinformatics 2009 25(12) 305-312 The Future

  45. Existing Drugs 3. Protein-ligand Docking … TB Protein-drug Interactome TB Structural Proteome 2. Binding site Similarity Drug resistance mechanism New therapeutics for MDR and XDR-TB Drugome/TB Target identification 1. Structural Determination & Modeling 4.2 Network Integration Drug repurposing TB Metabolome Side effect prediction TB Genome 4.1 Network Reconstruction Bioinformatics 2009 25(12) 305-312 The TB Druggome

  46. Predicted protein-ligand interaction network of M.tuberculosis. Proteins that are predicted to have similar binding sites are connected. Squares represent the top 18 most connected proteins. Bioinformatics 2009 25(12) 305-312 The TB Druggome

  47. Bioinformatics 2009 25(12) 305-312 The TB Druggome

  48. Some Limitations • Structural coverage of the given proteome • False hits / poor docking scores • Literature searching • It’s a hypothesis – need experimental validation • Money  Limitations

  49. 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 – cheminfomatics meets systems biology • Lots of other opportunities to examine existing drugs – DrugX and the Recovery Act

  50. Bioinformatics Final Examples.. • Donepezil for treating Alzheimer’s 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 Lots of Opportunities

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