Difference between revisions of "2020 AMS-535 Fall"

Dr. Guilherme Duarte Ramos Matos [631-632-8519, guilherme dot duarteramosmatos -at- stonybrook dot edu]

(1) pre-recorded oral presentations (25%)

• • each student will pre-record 2 ZOOM presentations based on papers in the schedule below
    

(2) class discussion (30%)

• • at scheduled class times, students will go into ZOOM breakout rooms
    

(3) take home quizzes (45%)

• • 5 quizzes will be given and the lowest quiz grade will be dropped

• 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

• Chemistry Review

1. Molecular structure, bonding, graphical representations

2. Functionality, properties of organic molecules

• Biomolecular Structure

1. Lipids, carbohydrates

2. Nucleic acids, proteins

• No Class: Labor Day

• Molecular Interactions and Recognition

1. Electrostatics, 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

2. OPLS

1. last, first

2. last, first

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

2. 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

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

2. 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

• Classical Force Fields

1. AMBER

• Explicit Solvent Models

2. Water models (TIP3P, TIP4P, SPC)

1. last, first

2. last, first

1. 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

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

1. 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. Condensed-phase calculations (DGhydration)

• Continuum Solvent Models

2. Generalized Born Surface Area (GBSA)

1. last, first

2. last, first

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

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

• Continuum Solvent Models

1. Poisson-Boltzmann Surface Area (PBSA)

2. Accuracy of partial atomic changes for GBSA and PBSA

1. last, first

2. last, first

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

2. Rizzo, R. C.; et al., Estimation of Absolute Free Energies of Hydration Using Continuum Methods: Accuracy of Partial Charge Models and Optimization of Nonpolar Contributions. J. Chem. Theory. Comput. 2006, 2, 128-139

SECTION III: SAMPLING METHODS

• Molecular Conformations

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

• Sampling Methods for Large Simulations

2. Molecular dynamics (MD)

1. last, first

2. last, first

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

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

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

• Sampling Methods for Large Simulations

1. Monte Carlo (MC)

• Predicting Protein Structure

2. Ab initio structure prediction (protein-folding)

1. last, first

2. last, first

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

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

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

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

• Predicting Protein Structure

1. Example Trp-cage

2. Comparative (homology) modeling

1. last, first

2. last, first

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

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

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

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

• Predicting Protein Structure

1. Case studies (CASP)

2. Accelerated MD for Blind Protein Prediction

1. last, first

2. last, first

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

2. Perez, A.; et al., Blind protein structure prediction using accelerated free-energy simulations. Sci. Adv. 2016, 2

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

• Predicting Protein Structure

1. MD x-ray refinement

2. Protein Design

1. last, first

2. last, first

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

1. Kuhlman, A. T.;et al, Design of a Novel Globular Protein Fold with Atomic-Level Accuracy. Science 2003, 302, 1364-1368

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

SECTION IV: LEAD DISCOVERY

• Docking

1. Introduction to DOCK

2. Test Sets (pose reproduction)

1. last, first

2. last, first

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

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

2. The CCDC/Astex Test Set

• Docking

1. Test Sets (virtual screening)

2. Test Sets (database enrichment)

1. last, first

2. last, first

1. 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

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

1. ZINC Website at UCSF, Shoichet group

• Docking

1. Footprint-based scoring

• Discovery Methods

2. Hotspot probes (GRID)

1. last, first

2. last, first

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.

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

• Discovery Methods

1. COMFA

2 Pharmacophores

1. last, first

2. last, first

1. Kubinyi, H., Comparative molecular field analysis (CoMFA). Encyclopedia of Computational Chemistry, Databases and Expert Systems Section, John Wiley & Sons, Ltd. 1998

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

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.

1. Pharmacophores

2. De novo design

1. last, first

2. last, first

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

2. Cheron, N.; et al., OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands. J. Med. Chem. 2016, 59, 4171-4188

• Discovery Methods

1. De novo design

2. Genetic Algorithm

1. last, first

2. last, first

1. 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

SECTION V: LEAD REFINEMENT

• Free Energy Methods

1. Thermolysin with two ligands (FEP)

2. Fatty acid synthase I ligands (TI)

1. last, first

2. last, first

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

2. 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

1&2. Jorgensen, W. L., Free Energy Calculations: A Breakthrough for Modeling Organic Chemistry in Solution. Accounts Chem. Res. 1989, 22, 184-189

1&2. Kollman, P., Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93, 2395-2417

2. 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

• MM-PB/GBSA

1. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods

• MM-GBSA case studies

2. EGFR and mutants

1. last, first

2. last, first

1. 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

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

• MM-GBSA case studies

1. ErbB family selectivity

• Linear Response

2. Intro to Linear Response (LR method)

1. last, first

2. last, first

1. Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406

2. 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

• Linear Response

1. Inhibition of protein kinases (Extended LR method)

• Properties of Known Drugs

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

1. last, first

2. last, first

1. 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

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

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

• No Class: Thanksgiving

• No Class: Thanksgiving

• Properties of Known Drugs

1. Lipinski Rule of Five

2 ADME Prediction

1. last, first

2. last, first

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

• TBD

• TBD

• Final Exam