2020 AMS-535 Fall

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Please see http://ringo.ams.sunysb.edu/~rizzo for Rizzo Group Homepage


Instructor Dr. Robert C. Rizzo [631-632-8519, rizzorc -at- gmail.com]
TA Dr. Guilherme Duarte Ramos Matos [631-632-8519, guilherme dot duarteramosmatos -at- stonybrook dot edu]

John Bickel [631-632-8519, john dot bickel -at- stonybrook dot edu]

Course No. AMS-535 / CHE-535
Location/Time Online, Monday and Wednesday 2:40PM - 4:00PM
Office Hours Anytime by appointment, Math Tower 3-129


Date
Topic
Speaker and Presentation
Primary Reference
Secondary Reference
2019.08.24 Mon
  • Organizational Meeting
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2019.08.26 Wed
*Drug Discovery

SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE

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

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

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2019.08.31 Mon
  • Chemistry Review

1. Molecular structure, bonding, graphical representations
2. Functionality, properties of organic molecules
Rizzo, R.
presentation
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2019.09.02 Wed
  • Biomolecular Structure

1. Lipids, carbohydrates
2. Nucleic acids, proteins
Rizzo, R.
presentation
 structures of the 20 amino acid side chains
2019.09.07 Mon
  • No Class: Labor Day
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2019.09.09 Wed
  • 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
Rizzo, R.
presentation
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2019.09.14 Mon
  • Intro. to Methods in 3-D Structure Determination

1. Crystallography, NMR
2. Structure Quality, PDB in detail
Rizzo, R.
presentation
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2019.09.16 Wed
Quiz Prior Section I

SECTION II: MOLECULAR MODELING

  • Classical Force Fields

1. All-atom Molecular Mechanics
2. OPLS

1. Arachchi, Kalani

2. Chakraborti, Shreyoshi

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

2019.09.21 Mon
  • Classical Force Fields

1. AMBER



  • Explicit Solvent Models

2. Water models (TIP3P, TIP4P, SPC)

1. He, Miaomiao

2. Tan, Rodger

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

2019.09.23 Wed
  1. Condensed-phase calculations (DGhydration)


2. Zhang, Hong



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

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2019.09.28 Mon
  • Continuum Solvent Models
1. Generalized Born Surface Area (GBSA)
2. Poisson-Boltzmann Surface Area (PBSA)

1. Zhu, Chuanzhou

2. King, Morgan

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

1. and 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

2019.09.30 Wed
Quiz Prior Section II

SECTION III: SAMPLING METHODS

  • Molecular Conformation
1. Small molecules, peptides, relative energy, minimization methods

1. Zhang, Yunlei

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

2019.10.05 Mon
  • Primary Sampling Methods for Computer Simulations
1. Molecular dynamics (MD)
2. Monte Carlo (MC)

1. Wang, Hehe

2. Laverty, Scott

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

2019.10.07 Wed
  • Predicting Protein Structure I.
1. Ab initio prediction (protein-folding)
2. Example Trp-cage

1. Earlie, Ethan

2. Faizi, Aymon

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

2019.10.12 Mon
  • Predicting Protein Structure II.
1. Comparative (homology) modeling
2. Case studies (CASP)

1. Moalemi, Debbi

2. Pipitone, Karli

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

2019.10.14 Wed
  • Predicting Protein Structure Part III
1. Accelerated MD for Blind Protein Prediction
2. MD x-ray refinement

1. Stepanenko, Darya

2. Ertem, Fatma

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

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

2. 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

2019.10.19 Mon
Quiz Prior Section III


SECTION IV: LEAD DISCOVERY

  • Docking I.
1. Introduction to DOCK

1. Telehany, Stephen

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

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

2019.10.21 Wed
  • Docking II.
1. Test Sets (binding modes)
2. Test Sets (virtual screening)

1. Samoilova, Khristina

2. He, Miaomiao

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

2019.10.26 Mon
  • Docking III.
1. Database Enrichment
2. Footprint-based scoring

1. Chakraborti, Shreyoshi

2. Zhang, Hong

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

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

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2019.10.28 Wed
  • Discovery Methods I.
1. Hotspot probes (GRID)
2. COMFA

1. Zhu, Chuanzhou

2. Arachchi, Kalani

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., Comparative molecular field analysis (CoMFA). 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

2019.11.02 Mon
  • Discovery Methods II.
1. Pharmacophores in drug design #1
2. Pharmacophores in drug design #2

1. McHugh, Ryan

2. King, Morgan

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

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

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2019.11.04 Wed
  • Discovery Methods III.
1. De novo design
2. Genetic Algorithm

1. & 2. Presentation

Prentis, Lauren

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

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

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

2019.11.09 Mon
Quiz Prior Section IV

SECTION V: LEAD REFINEMENT

1. Free Energy Perturbation (FEP)
2. Thermolysin with two ligands

1. Wang, Hehe

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

2019.11.11 Wed
  • Thermodynamic integration
  • MM-PB/GBSA
1. Free energy calculation using TI
2. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods

1. Zhang, Yunlei

2. Moalemi, Debbi

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

2019.11.16 Mon
  • MM-GBSA case studies
1. EGFR and mutants
2. ErbB family selectivity

1. Faizi, Aymon

2. Earlie, Ethan

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

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

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2019.11.18 Wed
  • Linear Response
1. Intro to Linear Response (LR method)
2. Inhibition of protein kinases (Extended LR method)

1. Stepanenko, Darya

2. Fatma, Ertem

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

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2019.11.23 Mon
  • No Class: Thanksgiving
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2019.11.23 Wed
  • No Class: Thanksgiving
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2019.11.30 Mon
  • Properties of Known Drugs
1. Molecular Scaffolds (frameworks) and functionality (side-chains)
2. Lipinski Rule of Five

1. Basu, Rajeswari

2. Samoilova, Khristina

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

2019.12.02 Wed
  • Review for Final Exam
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 final_exam_study_guide
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2019.12.07 Mon
  • Review for Last Quiz
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final_exam_study_guide Thermodynamic Cycles

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  • Final Exam
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 No Final Exam in AMS-535/CHE-535 for Fall 2020
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