Drug Discovery: Proteomics, Genomics
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The document discusses drug discovery approaches using omics data and computational methods. It provides two examples: 1) Repositioning the Parkinson's disease drugs entacapone and tolcapone to treat tuberculosis by identifying a similar binding site on the M. tuberculosis enzyme InhA. 2) Explaining the side effects of the drug torcetrapib in a clinical trial for heart disease by identifying off-target binding to nuclear receptors like PPAR through structural analysis and docking simulations. The document advocates a network pharmacology approach considering multiple drug targets.
![Drug Discovery: Proteomics, Genomics Philip E. Bourne Professor of Pharmacology UCSD [email_address] 858-534-8301 SPPS273 01/17/12](https://image.slidesharecdn.com/bournedrugddc-120117100452-phpapp02/85/Drug-Discovery-Proteomics-Genomics-1-320.jpg)
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Drug Discovery: Proteomics, Genomics
- 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
- 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
- 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
- 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
- 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
- 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)
- 35. Looking at the Problem on a Large Scale 01/17/12 SPPS273
- 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).
- 41. Example 2 - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387 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
- 49. Questions? [email_address] 01/17/12 SPPS273
Editor's Notes
- 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)