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7. Molecular Docking and Drug Discovery

7. Molecular Docking and Drug Discovery. R. M. ChemDB RChemDB. Filters. Experiments. NM. The Docking Problem. Given : receptor binding pocket and ligand. Task : quickly find correct binding pose. Two critical modules: Search Algorithm Scoring Function. Definitions.

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7. Molecular Docking and Drug Discovery

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  1. 7. Molecular Docking and Drug Discovery

  2. R M ChemDB RChemDB Filters Experiments NM 2

  3. The Docking Problem • Given: receptor binding pocket and ligand. • Task:quickly find correct binding pose. Two critical modules: • Search Algorithm • Scoring Function 3

  4. Definitions • pKd = measures tightness of binding • pKi = measures ability to inhibit • Mechanisms of action—for instance: • Competitive inhibition (most typical docking case) • Allosteric inhibition (bind to different pocket) • Allosteric activation 4

  5. Challenges • Search algorithm • Speed (5M compounds or more) • Local minima • High-dimensional search space • Scoring function • Strict control of false positives • Good correlation with pKd • Multiple terms • No consensus • Non-additive effects (solvation, hydrophobic interactions) • Note: pKd does not always correspond with activity • ADME concerns 5

  6. Examples of Docking Search Algorithms • Genetic Algorithms • Incremental Construction • Fragment Reconstruction • Gradient Descent • Simulated Annealing and other MC Variants • Tiered Scoring Functions • fast screening functions • slow accurate functions 6

  7. High Dimensionality: Flexibility • Most algorithms handle ligand flexibility but do NOT handle receptor flexibility. • Iterative Docking to find alternate conformations of the protein • Dock flexible ligand • Minimize receptor holding ligand rigid • Repeat 7

  8. Scoring Function • Energy of Interaction (pKd) • Electrostatics • Van der Waal’s interactions • Hydrogen bonds • Solvation effects • Loss of entropy • Active site waters 8

  9. ADME ADME concerns can be more important than bioactivity. Most of these properties are difficult to predict. • Absorption • Distribution • Metabolism • Excretion 9

  10. Docking Programs • Dock (UCSF) • Autodock (Scripps) • Glide (Schrodinger) • ICM (Molsoft) • FRED (Open Eye) • Gold, FlexX, etc. 10

  11. Evaluation of Docking Programs • Evaluation of library ranking efficacy in virtual screening. J Comput Chem. 2005 Jan 15;26(1):11-22. • Evaluation of docking performance: comparative data on docking algorithms. J Med Chem. 2004 Jan 29;47(3):558-65. • Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors. J Chem Inf Comput Sci. 2004 May-Jun;44(3):1123-9. 11

  12. Cluster Based Computing • Trivially parallelizable • Divide ligand input files • Some programs have specific parallel implementations (PVM or MPI implementations,…) • Commercial licenses are expensive 12

  13. Consensus Scoring • Combining independent scoring functions and docking algorithms can improve results • Most common method: sort using the sum of the ranks of component scores • More sophisticated methods exist Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model. 2005 Jul-Aug;45(4):1134-46. 13

  14. Adding Chemical Informatics Docking results can be improved by using chemical information about the hits. Chemicals which bind the same protein tend to have similar structure. Iterating back and forth between docking and searching large DB. Use other filters and predictive modules (e.g. Lipinski rules) ALGORITHM: • Dock and rank a chemical database • Create a bayesian model of the fingerprints of the top hits. • Re-rank the database based on their likelihood according to the bayesian model • Finding More Needles in the Haystack: A Simple and Efficient Method for Improving High-Throughput Docking Results J. Med. Chem., 47 (11), 2743 -2749, 2004. 14

  15. Visualization • Viewers must be able to scroll through tens or hundreds of small molecule hits • Accessible viewers designed for this problem: • VIDA from OpenEye (free for academics) • ViewDock module of Chimera from UCSF (free, open source) 15

  16. Long-term Goal of Drug Discovery • LTDD (Low Throughput Drug Design) instead of HTVS (High Throughput Virtual Screening) • Common ground: explore virtual space 16

  17. Drug DiscoveryCase Study: Tuberculosis

  18. Tuberculosis Mycobacterium Tuberculosis Very thick, waxy cell wall 18

  19. The Cell Wall: Key to Pathogen Survival • Tuberculosis • 7th cause of death • 1 in 3 people have TB • Leading AIDS death cause • Multi-drug resistant • Mycobacterium tuberculosis >30 C fatty acid 10% of genome Sugar 6 different ACCase b subunits, AccD1-6 Acyl-CoA Homologs of PccB Focus on AccD4-6 Cell wall lipids: Important for pathogen virulence, survival and latency 19

  20. Tuberculosis (TB): An old foe 20

  21. The White Death John Keats 1795-1821 Frederic Chopin 1810-1849 21

  22. TB: still a real threat, because….. Multi-Drug Resistant (Super TB strain) Its ability to stay alive 22

  23. The Cell Wall: Key to Pathogen Survival >30 C fatty acid • Tuberculosis • 7th cause of death worldwide • 1 in 3 people have TB • Leading cause AIDS death • Multi-drug resistant • Mycobacterium • tuberculosis 10% of genome Sugar 6 different ACCase b subunits, AccD1-6 Acyl-CoA Homologs of PccB Focus on AccD4-6 Cell wall lipids: Important for pathogen virulence, survival and latency Substrate specificity for AccD4-6? 23

  24. AccD5 Protein Structures AccD4 (3.3 Å) Solved AccD5 (2.9 Å) AccD6 (2.7 Å) 24

  25. Structure of AccD5 25

  26. Structure-Based Drug Design Enzyme assay Crystals & Crystal structure 3. Combinatorial chemistry 1. High throughput screening TB ACCase, AccD5 26 2. Virtual Screening Lead compound

  27. The Computational/Experimental Loop Similarity Search Docking Assay 27

  28. Docking Results • Diversity set (1990) from NCI 28

  29. 300uM 50uM 300uM 50-100uM NCI 65828 (Lead 1) NCI 172033 (Lead 2) 29

  30. Structure-Based Drug Design Identified AccD5 Inhibitors KI = 4.7 mM, KGI = ~50 mM  New TB drug lead 30 T. Lin, M. Melgar, S. J. Swamidass, J. Purdon, T. Tseng, G. Gago, D. Kurth, P. Baldi, H. Gramajo, and S. Tsai. PNAS, 103, 9, 3072-3077, (2006). US Patent pending.

  31. Acknowledgements • Pharmacology • Daniele Piomelli • Chemistry • G. Weiss • J. S. Nowick • R. Chamberlin • S. Tsai • K. Shea • Informatics • Liva Ralaivola • J. Chen • S. J. Swamidass • Yimeng Dou • Peter Phung • Jocelyne Bruand • Chloe Azencott • Alex Ksikes • Ryan Allison • Funding • NIH • NSF • Sun • IGB 31

  32. Two Strategies • Chemical similarity: • Docking: 32

  33. AccD5 • Enzyme necessary for mycolic acid biosynthesis in M. tuberculosis. 33

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