CADD Lecture

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Slides from a guest lecture on computer-aided drug design in the Spring 2012 Macromolecular Modeling course at Georgia Tech

Transcript of CADD Lecture

  • 1. COMPUTER-AIDEDDRUG DESIGNThursday, April 19, 2012Kazi Shefaet

2. Outline The pharmaceutical pipeline Structure-based drug design: docking Ligand-based drug design: pharmacophore modeling and QSAR 3. What is a drug? A substance that, when absorbed,alters normal bodily functionIn pharmacology: FDA-approved for the diagnosis, treatment, or prevention of disease. 4. Major Drug Classes Small Molecule ProteinVaccineAspirinInsulin Lovastatin TrastuzumabAmprenavirErythropoietin 5. Where do drugs come from?VaccinesNatural4% 5%Biologics14% Natural Derivatives23% Synthetic Synthetic 40% Natural Mimics14%Newnan & Cragg. J Nat Prod 70, 461477 (2007) 6. Natural Sources Aspirin is derived Taxol was discoveredMorphine was purifiedfrom willow barkin the Pacific yew treefrom opium poppies 7. Success Rate in Pharma10,0001,000100101marketable drug 8. Compound Libraries Commercial, government or 10,000 10,000,000academic compounds 9. Target Identification Farooque & Lee. Annu Rev Physiol 71, 465487 (2009) 10. Assay Development Stockwell. Nature 432, 846-854 (2004) 11. Ligand Design Pathway Identify High-in vitroValidate Compoundthroughputlibraries screeningin vivoDevelop assay Target Discovery Lead Discovery Lead Optimization 12. Computer-Aided Drug Design Enrich existing compound libraries Reduce amount of chemical waste Faster progress Lower costs 13. Computers in the Ligand Design PathwayIdentifyHigh-in vitro Validate Compound throughput librariesscreeningin vivo DevelopassayTarget Discovery Lead Discovery Lead OptimizationBioinformaticsComputer-Aided Drug Design 14. Structure-based and Ligand-based Receptor Structure? KnownUnknownLigand-Based DesignStructure-Based Design e.g. pharmacophore modeling &e.g. Docking QSAR 15. Video: Molecular Docking using Glide 16. Protein-Ligand Docking For a receptor-ligand complex, we want to predict:1. Preferred orientation (pose)2. Binding affinity (score) A docking program has least two functional components1. Search algorithm2. Scoring function Docking can be used for virtual screening, lead optimization, or de novo design of ligands 17. Molecular Representations in Docking1.Atomic Every atom represented Usually used with a molecular mechanics scoring function Computationally complex2.Surface Molecules represented by solvent excluded surface Align points by minimizing surface angle Commonly used in protein-protein docking3.Grid Receptors energetic contributions stored in grid points van der Waals, electrostatic, H-bonding terms May be combined with atomistic representation at bindingsite surface 18. Search Algorithms: Ligand Flexibility1.Systematic DOCK FlexX Cycle through values for each degree of freedom Glide Quickly leads to combinatorial explosion! Hammerhead Often implemented in anchor-and-grow algorithms FLOG2.Stochastic AutoDock MOE-Dock Make random changes, and evaluate using Monte Carlo or genetic algorithms GOLD Tabu search algorithms minimize repetition of dead-ends PRO_LEADS3.Simulation DOCK Glide Molecular dynamics MOE-Dock Energy minimization often used with other search AutoDocktechnique Hammerhead 19. Alchemical Free Energy Calculations GILaq + PaqGbind Paq + LgGII Gbind = GI GII(PL)aq 20. Scoring Functions1. Force-field-based D-Score Quantify sum of receptor-ligand interaction energy and internal ligand G-Score energy GOLD Ligand-receptor potential contains van der Waals and Coulomb AutoDock electrostatic terms (and H-bonding, in some cases) DOCK Limited by lack of solvation and entropic terms2. Empirical LUDI Binding energies as sums of uncorrelated terms, similar to but simpler F-Score than force-field terms ChemScore Parameterized to fit regression analysis of experimental data SCORE Often contain terms to approximate (de)solvation and entropic penalties Fresno X-Score1. Knowledge-based PMF Use potentials of mean force derived from libraries of protein-ligand complexes DrugScore Computationally simple SMoG 21. Force-field-based Scoring FunctionAutodock v4.0Kitchen et al. Nat Rev Drug Discov 3, 935949 (2004) 22. Empirical Scoring Functions LUDI Bhm. J Comput Aided Molec Des 8, 243-256 (1994) ChemScore Eldridge et al. J Comput Aided Molec Des 11, 425-444 (1997) 23. Knowledge-Based Scoring FunctionsExample: ligand carboxyl O to protein histidine NProcedure:1.Find all PDB structures with ligand carboxyl O2.Compute all distances to protein histidine Ns3.Plot histogram of all O-N distances: p(rO-N)4.Calculate E(r) using inverse BoltzmannBoltzmann: p(r) ~ exp[ -E(r)/(RT) ]Inverse Boltzmann: E(r) = -RT ln[ p(r) ]Muegge & Martin. J Med Chem 42, 791-804 (1999) 24. Scoring: General Caveats Ligand flexibility and size For rigid molecules, correct pose predicted 90-100% of the time Drops to 45-80% for molecules with more rotatable bonds and MW Binding strength Most strong binders (Kd