Drug Discovery: Proteomics, Genomics

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Guest Lecture in course SPPS 273 at the University of California San Diego

Guest Lecture in course SPPS 273 at the University of California San Diego

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  • 2D hyperbolic view of the phylogenetic tree, colored based on the origin of sequences (red, ocean data set from CVI; blue, NCBI NR) Alignment performed by MUSCLE from sequences identified in a joined ocean80_nr80 database by PDB-BLAST search. Visualization by HyperTree program from Sugen
  • Absorption, distribution, metabolism and excretion
  • Tuberculosis, which is caused by the bacterial pathogen Mycobacterium tuberculosis , is a leading cause of mortality among the infectious diseases. It has been estimated by the World Health Organization (WHO) that almost one-third of the world's population , around 2 billion people, is infected with the disease. Every year, more than 8 million people develop an active form of the disease, which claims the lives of nearly 2 million. This translates to over 4,900 deaths per day , and more than 95% of these are in developing countries. Despite the current global situation, antitubercular drugs have remained largely unchanged over the last four decades. The widespread use of these agents has provided a strong selective pressure for M.tuberculosis, thus encouraging the emergence of resistant strains. Multidrug resistant (MDR) tuberculosis is defined as resistance to the first-line drugs isoniazid and rifampin . The effective treatment of MDR tuberculosis necessitates long-term use of second-line drug combinations , an unfortunate consequence of which is the emergence of further drug resistance. Enter extensively drug resistant (XDR) tuberculosis - M.tuberculosis strains that are resistant to both isoniazid plus rifampin, as well as key second-line drugs . Since the only remaining drug classes exhibit such low potency and high toxicity , XDR tuberculosis is extremely difficult to treat. The rise of XDR tuberculosis around the world imposes a great threat on human health , therefore reinforcing the development of new antitubercular agents as an urgent priority. Very few Mtb proteins explored as drug targets
  • Superimposition of the binding sites of COMT and ENR COMT is show in green, its SAM co-factor is shown in yellow, and its BIE substrate is shown in purple. ENR is shown in blue, its NAD co-factor is shown in orange, and its 641 substrate is shown in red. Protein sequences were aligned according to the NAD and SAM co-factors. Similarities in electrostatic potential were also observed in the substrate binding pockets of COMT and ENR.
  • 3,996 proteins in TB proteome 749 solved structures in the PDB, representing a total of 284 proteins (7.2% coverage) ModBase contains homology models for entire TB proteome 1,446 ‘high quality’ homology models were added to the data set Structural coverage increased to 43.8% Retained only those models with a model score of > 0.7 and a Modpipe quality score of > 1.1 (2818 models). There were multiple models per protein. For each TB protein, chose the model with the best model score, and if they were equal, chose the model with the best Modpipe quality score (1703 models). However, 251 (+6) models were removed since they correspond to TB proteins that already have solved structures. 1446 models remained) Score for the reliability of a Model, derived from statistical potentials (F. Melo, R. Sanchez, A. Sali,2001 PDF ). A model is predicted to be good when the model score is higher than a pre-specified cutoff (0.7). A reliable model has a probability of the correct fold that is larger than 95%. A fold is correct when at least 30% of its Calpha atoms superpose within 3.5A of their correct positions. The ModPipe Protein Quality Score is a composite score comprising sequence identity to the template, coverage , and the three individual scores evalue , z-Dope and GA341 . We consider a MPQS of >1.1 as reliable
  • (nutraceuticals excluded)

Transcript

  • 1. Drug Discovery: Proteomics, Genomics Philip E. Bourne Professor of Pharmacology UCSD [email_address] 858-534-8301 SPPS273 01/17/12
  • 2. It Was the Best of Times, It Was the Worst of Times 01/17/12 SPPS273
  • 3. OMICS - The Best of Times 01/17/12 SPPS273
  • 4. The Worst of Times 01/17/12 SPPS273 Source: http://www.pharmafocusasia.com/strategy/drug_discovery_india_force_to_reckon.htm
  • 5. Stated Another Way
    • Glass ½ Empty : drug discovery in the traditional sense is in a woeful state
    • Glass ½ Full :
      • We have an explosion of data and hence a new emerging understanding of complex biological systems
      • Information technology is advancing rapidly
    • Let optimism rule – let IT, traditional computational chemistry and cheminfomatics meet bioinformatics, systems biology and information science to discover drugs in new ways – Systems Pharmacology
    SPPS273 The Take Home Message 01/17/12 Let Optimism Rule
  • 6. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 7. My Perspective/Bias
    • We work in the area of structural bioinformatics & more broadly computational biology
    • We distribute the equivalent to ¼ the Library of Congress to approx. 300,000 scientists each month
    • We are interested in improving the drug discovery process through computationally driven hypotheses on the complete biological system – systems pharmacology
    • Personally:
      • Open science advocate
      • Started 4 companies
      • Spent whole life in the ivory tower
    SPPS273 My Perspective/Bias 01/17/12
  • 8. The Omics Driver
    • DNA sequence data are doubling every 5 months
    • Funders are demanding data sharing plans
    • The long tail is neglected
    On the Future of Genomic Data Science 11 February 2011: vol. 331 no. 6018 728-729 01/17/12 SPPS273
  • 9. Its Not Just About Numbers its About Complexity Number of released entries Year The Omics Revolution Courtesy of the RCSB Protein Data Bank 01/17/12 SPPS273
  • 10. The Omics Revolution in One Slide Biological Experiment Data Information Knowledge Discovery Collect Characterize Compare Model Infer Sequence Structure Assembly Sub-cellular Cellular Organ Higher-life Year 90 05 Computing Power Sequencing Data 1 10 100 1000 10 5 95 00 Human Genome Project E.Coli Genome C.Elegans Genome 1 Small Genome/Mo. ESTs Yeast Genome Gene Chips Virus Structure Ribosome Model Metaboloic Pathway of E.coli Complexity Technology Brain Mapping Genetic Circuits Neuronal Modeling Cardiac Modeling Human Genome # People /Web Site 10 6 10 2 1 Virtual Communities 10 6 Blogs Facebook 1000 ’s GWAS The Omics Revolution 01/17/12 SPPS273
  • 11. Metagenomics
    • New type of genomics
    • New data (and lots of it) and new types of data – Initial ocean survey
      • 17M new (predicted proteins!) 4-5 x growth in just few months and much more coming
      • New challenges and exacerbation of old challenges
    The Omics Revolution 01/17/12 SPPS273
  • 12. Metagenomics: Early Results
    • More then 99.5% of DNA in very environment studied represent unknown organisms
      • Culturable organisms are exceptions, not the rule
    • Most genes represent distant homologs of known genes, but there are thousands of new families
    • Everything we touch turns out to be a gold mine
    • Environments studied:
      • Water (ocean, lakes)
      • Soil
      • Human body (gut, oral cavity, human microbiome)
    The Omics Revolution 01/17/12 SPPS273
  • 13. Metagenomics New Discoveries Environmental (red) vs. Currently Known PTPases (blue) Higher eukaryotes 1 2 3 4 The Omics Revolution 01/17/12 SPPS273
  • 14. Warning: With Explosive Growth Comes Problems: Currently 30% of Functional Annotations in Databases May be Wrong 01/17/12 SPPS273
  • 15. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 16. Towards Open Science
    • Open access publishing
    • Open source software
    • Generation of scientists weaned on social networks
    • Blogs, wikis, social bookmarking etc. are becoming a valid form of scientific discourse
    SPPS273 http://www.osdd.net/ 01/17/12
  • 17. An Exemplar of Open Science www.sagebase.org 01/17/12 SPPS273
  • 18. There is a Battle Going On as We Speak 01/17/12 SPPS273
  • 19. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 20. 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 01/17/12 SPPS273
  • 21. Why Don’t 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 01/17/12 SPPS273
  • 22. 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
    01/17/12 SPPS273
  • 23. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 24. 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?
    Exploiting the Structural Proteome 01/17/12 SPPS273
  • 25. What Do These Off-targets Tell Us?
    • Potentially many things:
      • Nothing
      • How to optimize a NCE
      • A possible explanation for a side-effect of a drug already on the market
      • A possible repositioning of a drug to treat a completely different condition
      • The reason a drug failed
      • A multi-target strategy to attack a pathogen
    Exploiting the Structural Proteome 01/17/12 SPPS273
  • 26. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Exploiting the Structural Proteome 01/17/12 SPPS273 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
  • 27. 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 … Exploiting the Structural Proteome 01/17/12 SPPS273 Xie and Bourne 2009 Bioinformatics 25(12) 305-312
  • 28. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 29. The Problem with Tuberculosis
    • One third of global population infected
    • 1.7 million deaths per 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
    Example 1 – Repositioning The TB Story 01/17/12 SPPS273
  • 30. 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
    Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Example 1 – Repositioning The TB Story 01/17/12 SPPS273
  • 31. Functional Site Similarity between COMT and InhA
    • 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
    • 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 ...
    Repositioning - The TB Story Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 01/17/12 SPPS273
  • 32. Binding Site Similarity between COMT and InhA Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Example 1 – Repositioning The TB Story 01/17/12 SPPS273 COMT SAM (cofactor) BIE (inhibitor) NAD (cofactor) InhA 641 (inhibitor)
  • 33. 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 Example 1 – Repositioning The TB Story 01/17/12 SPPS273
  • 34. 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
    Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423 Example 1 – Repositioning The TB Story 01/17/12 SPPS273
  • 35. Looking at the Problem on a Large Scale 01/17/12 SPPS273
  • 36. 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
  • 37. 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
  • 38. 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).
  • 39. New Ways of Thinking
    • Polypharmacology – One or multiple drugs binding to multiple targets for a collective effect aka Dirty Drugs
    • Network Pharmacology – Measuring that effect on the whole biological network
    SPPS273 01/17/12
  • 40. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 41. Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387 01/17/12 SPPS273
  • 42. 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.
    HDL LDL CETP CETP inhibitor X Bad Cholesterol Good Cholesterol PLoS Comp Biol 2009 5(5) e1000387 Example 2 - The Torcetrapib Story 01/17/12 SPPS273
  • 43. Docking Scores eHits/Autodock PLoS Comp Biol 2009 5(5) e1000387 Example 2 - The Torcetrapib Story 01/17/12 SPPS273 Off-target PDB Ids Torcetrapib Anacetrapib JTT705 Complex ligand CETP 2OBD -11.675 / -5.72 -11.375 / -8.15 -7.563 / -6.65 -8.324 (PCW) Retinoid X receptor 1YOW 1ZDT -11.420 / -6.600 -6.74 -8.696 / -7.68 -7.35 -6.276 / -7.28 -6.95 -9.113 (POE) PPAR delta 1Y0S -10.203 / -8.22 -10.595 / -7.91 -7.581 / -8.36 -10.691(331) PPAR alpha 2P54 -11.036 / -6.67 -0.835 / -7.27 -9.599 / -7.78 -11.404(735) PPAR gamma 1ZEO -9.515 / -7.31 > 0.0 / -8.25 -7.204 / -8.11 -8.075 (C01) Vitamin D receptor 1IE8 >0.0/ -4.73 >0.0 / -6.25 -6.628 / -9.70 -8.354 (KH1) -7.35 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/ -4.33 >0.0/-6.13 /-6.40 >0.0/ -7.81 >0.0/ -6.98 /-7.64 -7.191 / -8.49 /-6.33 /6.35 ??? T-Cell CD1B 1GZP -8.815 / -7.02 -13.515 / -7.15 -7.590 / -8.02 -6.519 (GM2) IL-10 receptor 1LQS / -4.59 / -6.77 / -5.95 ??? GM-2 activator 2AG9 -9.345 / -6.26 -9.674 / -6.98 -8.617 / -6.17 ??? (MYR) -4.16 (3CA2+) CARDIAC TROPONIN C 1DTL /-5.83 /-6.71 /-5.79 cytochrome bc1 complex 1PP9 (PEG) /-6.97 /-9.07 /-6.64 1PP9 (HEM) /-7.21 /8.79 /-8.94 human cytoglobin 1V5H /-4.89 /-7.00 /-4.94
  • 44. RAS PPAR α RXR VDR + – High blood pressure FABP FA + Anti-inflammatory function ? Torcetrapib Anacetrapib JTT705 JNK/IKK pathway JNK/NF- K B pathway ? Immune response to infection JTT705 PPAR δ PPAR γ ? PLoS Comp Biol 2009 5(5) e1000387 Example 2 - The Torcetrapib Story 01/17/12 SPPS273
  • 45. Modifications to Early Stage Drug Discovery SPPS273 http://www.celgene.com/images/celgene_drug_arrow.gif 01/17/12 Off-targets Systems Biology
  • 46. Agenda
    • Where my perspective comes from
    • The omics revolution
    • The Open Science & IT revolutions
    • The impact on drug discovery
    • Applying the new biology to drug discovery
      • Example 1 – Drug repositioning
      • Example 2 - Determining side-effects
    • Words of caution
    SPPS273 01/17/12
  • 47. Words of Caution
    • Mistrust of computational approaches
    • Bioinformatics was previously oversold
    • Omics was previously oversold
    • Openness is an alien culture to drug discovery
    SPPS273 01/17/12
  • 48. Further Reading
    • L. Xie, L. Xie, S.L. Kinnings and P.E. Bourne 2012 Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs, Annual Review of Pharmacology and Toxicology 52 : 361-379 [ PDF].
    • And references therein
    01/17/12 SPPS273
  • 49. Questions? [email_address] 01/17/12 SPPS273