Difference between revisions of "2010 AMS-535 Fall"

• Organizational Meeting

SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE

• Drug Discovery

1. Introduction, history, irrational vs. rational
2. Viral Target Examples

1. Jorgensen, W.L., The many roles of computation in drug discovery. Science 2004, 303, 1813-8

2. Kuntz, I. D., Structure-based strategies for drug design and discovery. Science 1992, 257, 1078-1082

• No Class: Labor Day

• Chemistry Review

1. Molecular structure, bonding, graphical representations
2. Functionality, properties of organic molecules

• Biomolecular Structure

1. Lipids, carbohydrates
2. Nucleic acids, proteins

• Molecular Interactions and Recognition

1. Electrostastics, VDW interactions, hydrophobic effect, molecular recognition (binding energy)
2. Inhibitors types: allosteric, transition state, covalent vs non-covalent, selective, competitive

• Intro. to Methods in 3-D Structure Determination

1. Crystallography, NMR
2. Structure Quality, PDB in detail

SECTION II: MOLECULAR MODELING

• Classical Force Fields

1. All-atom Molecular Mechanics

Guest Lecture

Balius, T.

1. Mackerell, A. D., Jr., Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 2004, 25, 1584-604

1. van Gunsteren, W. F.; et al., Biomolecular modeling: Goals, problems, perspectives. Angew. Chem. Int. Ed. Engl. 2006, 45, 4064-92

• Force Field Development

1. OPLS
2. AMBER

1. Akter, R.

2. Cao, Y.

1. Jorgensen, W. L.; et al., Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids. J. Am. Chem. Soc. 1996, 118, 11225-11236

2. Cornell, W. D.; et al., A Second Generation Force Field For the Simulation of Proteins, Nucleic Acids, and Organic Molecules. J. Am. Chem. Soc. 1995, 117, 5179-5197

1. Jorgensen, W. L.; et al., The Opls Potential Functions For Proteins - Energy Minimizations For Crystals of Cyclic-Peptides and Crambin. J. Am. Chem. Soc. 1988, 110, 1657-1671

2. Bayly, C. I.; et al., A Well-Behaved Electrostatic Potential Based Method Using Charge Restraints For Deriving Atomic Charges - the RESP Model. J. Phys. Chem. 1993, 97, 10269-10280

• Explicit Solvent Models

1. Water models (TIP3P, TIP4P, SPC)
2. Condensed-phase calculations (DGhydration)

1. Chen, J.

2. Conte, M.

1. Jorgensen, W. L.; et al., Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926-935

2. Jorgensen, W. L.; et al., Monte Carlo Simulation of Differences in Free Energies of Hydration. J. Chem. Phys. 1985, 83, 3050-3054

• Continuum Solvent Models

1. Generalized Born Surface Area (GBSA)
2. Poisson-Boltzmann Surface Area (PBSA)

1. Efaplomatides, C.

2. Fochtman, B.

1. Still, W. C.; et al., Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. J. Am. Chem. Soc 1990, 112, 6127-6129

2. Sitkoff, D.; et al., Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models. J. Phys. Chem. 1994, 98, 1978-1988

SECTION III: SAMPLING METHODS

• Molecular Conformation

1. Small molecules, peptides, relative energy, minimization methods

1. Gardin, J.

1. Howard, A. E.; Kollman, P. A., An analysis of current methodologies for conformational searching of complex molecules. J. Med. Chem. 1988, 31, 1669-75

1. Section 4 (PAGES 22-27) Colby College Molecular Mechanics Tutorial Introduction, 2004, Shattuck, T.W., Colby College

1. Holloway, M. K., A priori prediction of ligand affinity by energy minimization. Perspect. Drug Discov. Design 1998, 9-11, 63-84

• Primary Sampling Methods for Computer Simulations

1. Molecular dynamics (MD)
2. Monte Carlo (MC)

1. Maringano, D.

2. Yao, Y

1. Karplus, M.; Petsko, G. A., Molecular dynamics simulations in biology. Nature 1990, 347, 631-9

2. Metropolis Monte Carlo Simulation Tutorial, LearningFromTheWeb.net, Accessed Oct 2008, Luke, B.

2. Jorgensen, W. L.; TiradoRives, J., Monte Carlo vs Molecular Dynamics for Conformational Sampling. J. Phys. Chem. 1996, 100,14508-14513

2. Metropolis, N.;et al., Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21, 1087-1092

• Predicting Protein Structure I.

1. Ab initio prediction (protein-folding)
2. Example Trp-cage

1. Grinshpun, B.

2. Hancewicz, J.

1. Dill, K. A.; Chan, H. S., From Levinthal to pathways to funnels. Nat. Struct. Biol. 1997, 4, 10-19

2. Simmerling, C.;et al., All-atom structure prediction and folding simulations of a stable protein. J. Am. Chem. Soc. 2002, 124,11258-9

1-2. Daggett, V.; Fersht, A., The present view of the mechanism of protein folding. Nat. Rev. Mol. Cell Biol. 2003, 4, 497-502

• Predicting Protein Structure II.

1. Comparative (homology) modeling
2. Case studies (CASP)

1. Jee, J.

2. Jin, X.

1. Marti-Renom, M. A.; et al., Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 2000,29,291-325

2. Moult, J., A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 2005,15, 285-9

1. Fiser, A.; et al., Evolution and physics in comparative protein structure modeling. Acc. Chem. Res. 2002, 35, 413-21

2. Kryshtafovych, A.; et al., Progress over the first decade of CASP experiments. Proteins 2005, 61 Suppl 7, 225-36

• Enhanced Sampling Techniques

1. Simulated annealing
2. Protein Design

Guest Lecture

Au, L.

1. Brunger, A. T.;Adams, P. D., Molecular dynamics applied to X-ray structure refinement. Acc. Chem. Res. 2002, 35, 404-12

2. Street, A. G.; Mayo, S. L., Computational protein design. Structure. 1999, 7, 105-9

1. Adams, P. D.; et al., Extending the limits of molecular replacement through combined simulated annealing and maximum-likelihood refinement. Acta Crystallogr D Biol Crystallogr 1999, 55, 181-90

2. Lippow, S. M.; Tidor, B., Progress in computational protein design. Curr. Opin. Biotechnol. 2007, 18, 305-311

SECTION IV: LEAD DISCOVERY

• Docking I.

1. Introduction to DOCK

Guest Lecture

Mukherjee, S.

1. Ewing, T. J.; et al., DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des. 2001, 15, 411-28

1. Moustakas, D. T.; et al., Development and Validation of a Modular, Extensible Docking program: DOCK 5. J. Comput. Aided Mol. Des. 2006, 20, 601-619

• Discovery Methods I.

1. Hotspot probes (GRID)
2. COMFA

1. Lee, S.

2. Lei, L.

1. Goodford, P. J., A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-57

2. Kubinyi, H., Encyclopedia of Computational Chemistry, Databases and Expert Systems Section, John Wiley & Sons, Ltd. 1998

1. Cramer, R. D.; Patterson, D. E.; Bunce, J. D., Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110, 5959-5967

• Discovery Methods II.

1. Pharmacaphores in drug design
2. De nova design

1. Ashiru-Balogun, J.

2. Li, M.

1. Chang, C.; et al., Pharmacophore-based discovery of ligands for drug transporters. Advanced Drug Delivery Reviews 2006, 58, 1431-1450

2. Pegg, S. C.; Haresco, J. J.; Kuntz, I. D., A genetic algorithm for structure-based de novo design. J Comput Aided Mol Des 2001, 15, 911-33

• Docking II.

1. Test Sets (binding modes)
2. Test Sets (virtual screening)

Guest Lecture

Mukherjee, S.

1. Nissink, J. W. M.; et al., A new test set for validating predictions of protein-ligand interaction. Prot. Struct. Funct. Genetics 2002, 49, 457-471

2. Irwin, J. J.; Shoichet, B. K., ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177-82

1. The CCDC/Astex Test Set

2. ZINC - A free database of commercially-available compounds for virtual screening

• Docking III.

1. Enrichment and Rescoring

Guest Lecture Balius, T.

1. Huang, N.; et al., Benchmarking Sets for Molecular Docking. J. Med. Chem. 2006, 49(23), 6789-6801

2. Deng, Z; et al., Knowledge-Based Design of Target-Focused Libraries Using Protein-Ligand Interaction Constraints. J. Med. Chem. 2006, 49, 490-500

SECTION V: LEAD REFINEMENT

• Free Energy Perturbation (FEP)

1. Thermolysin with two ligands

1. Li, Z.

1. Bash, P. A.; Singh, U. C.; Brown, F. K.; Langridge, R.; Kollman, P. A., Calculation of the relative change in binding free energy of a protein-inhibitor complex. Science 1987, 235, 574-6

1. Jorgensen, W. L., Free Energy Calculations: A Breakthrough for Modeling Organic Chemistry in Solution. Accounts Chem. Res. 1989, 22, 184-189

1. Kollman, P., Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93, 2395-2417

• Thermodynamic Integration, MM-PB/GBSA

1. H5N1 Avian influenza N1-PVR
2. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods

1. Liao, J.

2. Liu, J.

1. Lawrenz, M.; et al., Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir. J. Chem. Theory Comput. 2009, 5, 1106-1116

2. Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S. H.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D. A.; Cheatham, T. E., Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Accounts Chem. Res. 2000, 33, 889-897

• Case study

1. TI and MM-PBSA
2. TI and MM-GBSA

1. Liu, Y.

2. Messina, D.

1. Kollman, P. A.; Gouda, H.;Kuntz, I. D.; Case, D. A.; Free Energy Calculations for Theophylline Binding to an RNA Aptamer: Comparison of MM-PBSA and Thermodynamic Integration Methods. Biopolymers. 2003, 68, 16-34

2. Cai, Y.; Schiffer, C. A.; Decomposing the Energetic Impact of Drug Resistant Mutations in HIV-1 Protease on Binding DRV. J. Chem. Theory Comput. 2010, 6, 1358-1368

• No Class: Following a Friday schedule

• MM-GBSA case studies

1. EGFR
2. HIVgp41

1. Spaqnuolo, L.

2. Van Wart, T.

1. Balius, T.; Rizzo, R. C.; Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry 2009, 48, 8435-8448

2. Strockbine, B.; Rizzo, R. C., Binding of Anti-fusion Peptides with HIVgp41 from Molecular Dynamics Simulations: Quantitative Correlation with Experiment. Prot. Struct. Funct. Bioinformatics 2007, 63, 630-642

• Linear Response

1. Intro to Linear Response (LR method)
2. Inhibition of protein kinases (Extended LR method)

1. Yang, R.

2. Hambardzhieva, E.

1. Aqvist, J.; Mowbray, S. L., Sugar recognition by a glucose/galactose receptor. Evaluation of binding energetics from molecular dynamics simulations. J Biol Chem 1995, 270, 9978-81

2. Tominaga, Y.; Jorgensen, W. L.; General model for estimation of the inhibition of protein kinases using Monte Carlo simulations. J. Med. Chem. 2004, 47, 2534-2549

• Properties of Known Drugs

1. Lipinski Rule of Five
2. ADME prediction

1. Yerramilli, V.

2. Yu, W.

1. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 2001, 46, 3-26

2. Hou, T. J.; Xu, X. J.; ADME evaluation in drug discovery. J. Mol. Model, 2002, 8, 337-349

1. Lipinski, C. A., Chris Lipinski discusses life and chemistry after the Rule of Five. Drug. Discov. Today 2003, 8, 12-6

2. Hou, T. J.; Xu, X. J.; AMDE Evaluation in drug discovery 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J. Chem. Inf. Comput. Sci., 2003, 43, 2137-2152

• Properties of Known Drugs and Protein Structure Prediction III.

1. Molecular Scaffolds (frameworks) and functionality (side-chains)
2. TBA

1. TBA

2. TBA.

1. Bemis, G. W.; Murcko, M. A., The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 1996, 39, 2887-93

1. Bemis, G. W.; Murcko, M. A., Properties of known drugs. 2. Side chains. J. Med. Chem. 1999, 42, 5095-9

2. TBA

2. TBA