Difference between revisions of "2012 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

Huang, Y.

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

1. OPLS
2. AMBER

1. Cao, Y.

2. Goldberg, A.

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, S.

2. Chen, Y.

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. Tithi, J.

2. Foda, Z.

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. Chen, Q.

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. He, L.

2. Huang, H.

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. Kasavajhala, K.

2. Krantsevich, A.

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.Guest Lecture

Huang, Y.

2. Krantsevich, N.

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

1.Li, X.

2.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. Looger, L. L.; Hellinga, H. W., Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics. J Mol Biol. 2001, 307, 429-45

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. Desmet, J.; et al., The dead-end elimination theorem and its use in protein side-chain positioning. Nature. 1992, 356, 539-42

SECTION IV: LEAD DISCOVERY

• Docking I.

1. Introduction to DOCK
2. Introduction to Virtual Screening

Guest Lecture

Zhou, Y.

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

2. Klebe, G.; et al., Virtual ligand screening: strategies, perspectives and limitations. Drug. Disc. Today. 2006, 11, 580-595

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

• Discovery Methods I.

1. Hotspot probes (GRID)
2. COMFA

1. Liu, S.

2. Mehter, A.

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

• No Class: Hurricane Sandy

• No Class: Hurricane Sandy

• Discovery Methods II & III.

1. Pharmacaphores in drug design
2. De novo design

1.Guest Lecture Jiang, L.

2.Guest Lecture Allen, W.

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

1. Alvarez, J.; et al., Pharmacophore-Based Molecular Docking to Account for Ligand Flexibility. Proteins 2003, 51, 172-188

2. Jorgensen, W.; et al., Efficient drug lead discovery and optimization. Acc. of Chem. Research 2009, 42 (6), 724-733

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

Fochtman, B.

1. Mukherjee, S.; et al., Docking Validation Resources: Protein Family and Ligand Flexibility Experiments. J. Chem. Info. Model. 2010, 50, 1986-2000

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 Website at UCSF, Shoichet group

• Docking III.

1. Database Enrichment
2. Footprint-based scoring

1. Guo, J.

2. Guest Lecture Zhou, Y.

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

1. Balius, T.E.; et al., Implementation and Evaluation of a Docking-Rescoring Method Using Molecular Footprint Comparisons. J. Comput. Chem. 2011, 32, 2273-2289.

SECTION V: LEAD REFINEMENT

• Free Energy Perturbation (FEP)

1. Thermolysin with two ligands

1. Ralph, B.

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. Free energy calculation using TI
2. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods

1. Guest Lecture Jiang, L.,

2. Gu, Z.

1. Labahn, A.; et al., Free energy calculations on the binding of novel thiolactomycin derivatives to E. coli fatty acid synthase I. Bioorg Med Chem. 2012, 20, 3446-53

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

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

• No Class: Thanksgiving US

• case studies

1. TI and MM-GBSA
2. HIVgp41

1. Shan, J.

2. Stenzoski, N.

1. 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

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. Suhandynata, R.

2. Wang, X.

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. Wu, X.

2. Yuan, C.

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

• Final Exam discussion.

1. Final Exam Study Guide Handout

1. Zhou, Y.

1. final_exam_study_guide