Drug Discovery: Proteomics, Genomics

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Drug Discovery: Proteomics, Genomics. Philip E. Bourne Professor of Pharmacology UCSD pbourne@ucsd.edu 858-534-8301. Agenda. Where my perspective comes from The interplay between omics, IT and drug discovery The omics revolution Changes in IT and open science and software licensing - PowerPoint PPT Presentation

Text of Drug Discovery: Proteomics, Genomics

  • Drug Discovery: Proteomics, GenomicsPhilip E. BourneProfessor of Pharmacology UCSDpbourne@ucsd.edu 858-534-8301*SPPS273


  • AgendaWhere my perspective comes fromThe interplay between omics, IT and drug discoveryThe omics revolutionChanges in IT and open science and software licensingApplying the new biology to drug discoveryExample 1 Drug repositioningExample 2 - Determining side-effects Words of cautionSPPS273*


  • Some BackgroundWe work in the area of structural bioinformaticsWe distribute the equivalent to the Library of Congress to approx. 250,000 scientists each monthWe are interested in improving the drug discovery process through computationally driven hypotheses on the complete biological system

    Personally:Open science advocateStarted 4 companies Spent whole life in the ivory towerSPPS273*The Source of My Perspective


  • Observations Glass Empty: drug discovery in the traditional sense is in a woeful stateGlass Full:We have an explosion of data and hence a new emerging understanding of complex biological systemsInformation technology is advancing rapidlyLet optimism rule let traditional computational chemistry and cheminfomatics meet bioinformatics, systems biology and information science to discover drugs in new waysSPPS273*The Take Home Message


  • Biological Experiment Data Information Knowledge DiscoveryCollect Characterize Compare Model Infer SequenceStructureAssemblySub-cellularCellularOrganHigher-lifeYear9005Computing PowerSequencingData110 10010001059500Human Genome ProjectE.ColiGenomeC.ElegansGenome1 Small Genome/Mo.ESTsYeastGenomeGene ChipsVirus StructureRibosomeModel Metaboloic Pathway of E.coliComplexityTechnologyBrain MappingGenetic CircuitsNeuronal ModelingCardiac ModelingHuman Genome# People/Web Site1061021VirtualCommunitiesThe Drivers of Change Data & IT106BlogsFacebook1000sGWASThe Omics Revolution

  • Number of released entriesYearIts Not Just About Numbers its About ComplexityThe Omics RevolutionCourtesy of the RCSB Protein Data Bank

  • *Metagenomics - 2007New type of genomicsNew data (and lots of it) and new types of data17M new (predicted proteins!) 4-5 x growth in just few months and much more comingNew challenges and exacerbation of old challengesThe Omics Revolution

  • *Metagenomics: Early ResultsMore then 99.5% of DNA in very environment studied represent unknown organismsCulturable organisms are exceptions, not the ruleMost genes represent distant homologs of known genes, but there are thousands of new families

    Everything we touch turns out to be a gold mineEnvironments studied:Water (ocean, lakes)SoilHuman body (gut, oral cavity, human microbiome)The Omics Revolution

  • *Metagenomics New DiscoveriesEnvironmental (red) vs. Currently Known PTPases (blue)Higher eukaryotes1234The Omics Revolution

  • *The Good News and the Bad NewsGood newsData pointing towards function are growing at near exponential ratesIT can handle it on a per dollar basisBad newsData are growing at near exponential ratesQuality is highly variableAccurate functional annotation is sparse The Omics Revolution

  • *Example of the Interplay Between Bioinformatics & Proteomics - The Structural Genomics Pipeline

    The Omics RevolutionStructural biology moves from being functionally driven to genomically drivenFill inprotein fold spaceRobotics-ve dataSoftware engineeringFunctional predictionNotnecessarily

  • Towards Open ScienceOpen access publishingOpen source softwareGeneration of scientists weaned on social networksBlogs, wikis, social bookmarking etc. are becoming a valid form of scientific discourse SPPS273*http://www.osdd.net/


  • University Tech Transfer Offices are Slow to Embrace this ChangeOvervalue disclosuresInability to market disclosures appropriatelyProtracted negotiations in a fast moving marketDisable rather than enable startups



  • So Why is All of This So Important to Drug Discovery?We are beginning to piece together a complex living system and we need to understand that to do betterSPPS273*


  • A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690Why Dont we Do Better?A Couple of ObservationsGene 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 24:805815

  • Why Dont we Do Better?A Couple of Observations 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

  • ImplicationsEhrlichs 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 nave in a complex system

  • So How Can We Exploit All The New Data We are Collecting on This Complex System?Lets Work Through a Couple of ExamplesSPPS273*


  • What ifWe 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?

    Exploiting the Structural Proteome

  • What Do These Off-targets Tell Us?Potentially many things:NothingHow to optimize a NCEA possible explanation for a side-effect of a drug already on the marketA possible repositioning of a drug to treat a completely different conditionThe reason a drug failed A multi-target strategy to attack a pathogen

    Exploiting the Structural Proteome

  • Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many ExamplesExploiting the Structural Proteome

    Generic NameOther NameTreatmentPDBidLipitorAtorvastatinHigh cholesterol1HWK, 1HW8TestosteroneTestosteroneOsteoporosis1AFS, 1I9J ..TaxolPaclitaxelCancer1JFF, 2HXF, 2HXHViagraSildenafil citrateED, pulmonary arterial hypertension1TBF, 1UDT, 1XOS..DigoxinLanoxinCongestive heart failure1IGJ


  • 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-targetsSearch for similar small moleculesDock molecules to both primary and off-targetsStatistics analysis of docking score correlationsExploiting the Structural Proteome


  • The Problem with TuberculosisOne third of global population infected1.7 million deaths per year95% of deaths in developing countriesAnti-TB drugs hardly changed in 40 yearsMDR-TB and XDR-TB pose a threat to human health worldwideDevelopment of novel, effective, and inexpensive drugs is an urgent priorityExample 1 Repositioning The TB Story

  • Found..Evolutionary linkage between: NAD-binding Rossmann foldS-adenosylmethionine (SAM)-binding domain of SAM-dependent methyltransferasesCatechol-O-methyl transferase (COMT) is SAM-dependent methyltransferaseEntacapone and tolcapone are used as COMT inhibitors in Parkinsons disease treatmentHypothesis:Further investigation of NAD-binding proteins may uncover a potential new drug target for entacapone and tolcaponeKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Functional Site Similarity between COMT and InhAEntacapone and tolcapone docked onto 215 NAD-binding proteins from different speciesM.tuberculosis Enoyl-acyl carrier protein reductase ENR (InhA) discovered as potential new drug targetInhA is the primary target of many existing anti-TB drugs but all are very toxicInhA catalyses the final, rate-determining step in the fatty acid elongation cycleAlignment of the COMT and InhA binding sites revealed similarities ...

    Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423

  • Binding Site Similarity between COMT and InhAKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Summary of the TB StoryEntacapone and tolcapone shown to have potential for repositioningDirect mechanism of action avoids M. tuberculosis resistance mechanismsPossess excellent safety profiles with few side effects already on the marketIn vivo supportAssay of direct binding of entacapone and tolcapone to InhA reveals a possible lead with no chemical relationship to existing drugsKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • Summary from the TB Alliance Medicinal ChemistryThe minimal inhibitory concentration (MIC) of 260 uM is higher than usually consideredMIC is 65x the estimated plasma concentrationHave other InhA inhibitors in the pipelineKinnings et al. 2009 PLoS Comp Biol 5(7) e1000423Example 1 Repositioning The TB Story

  • 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.

    The TB DruggomeBioinformatics 2009 25(12) 305-312

  • The TB DruggomeBioinformatics 2009 25(12) 305-312

  • SMAP p-value < 1e-5drugs

    TB proteinsp < 1e-7p < 1e-6p < 1e-5

    The TB Druggome

  • New Ways of Thinking

    Polypharmacology One or mul