2013 AMS-535 Fall

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Announcements: Fall 2013

Date
Topic
Speaker and Presentation
Primary Reference
Secondary Reference
2013.08.26 Mon
  • Organizational Meeting
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2013.08.28 Wed

SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE

  • Drug Discovery
  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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2013.09.02 Mon
  • No Class: Labor Day
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2013.09.04 Wed
  • Chemistry Review
  1. Molecular structure, bonding, graphical representations
  2. Functionality, properties of organic molecules
Rizzo, R.
presentation
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2013.09.09 Mon
  • Biomolecular Structure
  1. Lipids, carbohydrates
  2. Nucleic acids, proteins
Rizzo, R.
presentation
 structures of the 20 amino acid side chains
2013.09.11 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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2013.09.16 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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2013.09.18 Wed
Quiz Prior Section I

SECTION II: MOLECULAR MODELING

  • Classical Force Fields
  1. All-atom Molecular Mechanics

1. Kennedy, C.

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

2013.09.23 Mon
  1. OPLS
  2. AMBER

1. Heymann, J.

2. Lebedev, I.

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

2013.09.25 Wed
  • Explicit Solvent Models
  1. Water models (TIP3P, TIP4P, SPC)
  2. Condensed-phase calculations (DGhydration)

1. Li, F.

2. Pulkoski, 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

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2013.09.30 Mon
  • Continuum Solvent Models
  1. Generalized Born Surface Area (GBSA)
  2. Poisson-Boltzmann Surface Area (PBSA)

1. Sopp, J.

2. Yu, 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

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2013.10.02 Wed
Quiz Prior Section II

SECTION III: SAMPLING METHODS

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

1. Bai, L.

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

2013.10.07 Mon
  • Primary Sampling Methods for Computer Simulations
  1. Molecular dynamics (MD)
  2. Monte Carlo (MC)

1. Chu, W.

2. Hussein, K.

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

2013.10.09 Wed
  • Predicting Protein Structure I.
  1. Ab initio prediction (protein-folding)
  2. Example Trp-cage

1. Lichtenthal, B.

2. Liu, K.

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

2013.10.14 Mon
  • Enhanced Sampling Techniques
  1. Simulated annealing
  2. Protein Design

1. & 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

2013.10.16 Wed
  • Predicting Protein Structure II.
  1. Comparative (homology) modeling
  2. Case studies (CASP)

1. Pal, J.

2. Russo, A.

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

2013.10.21 Mon
Quiz Prior Section III


SECTION IV: LEAD DISCOVERY

  • Docking I.
  1. Introduction to DOCK

1. Sun, 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

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

2013.10.23 Wed
  • Docking II.
  1. Test Sets (binding modes)
  2. Test Sets (virtual screening)

1. & 2. 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

2013.10.28 Mon
  • Docking III.
  1. Database Enrichment
  2. Footprint-based scoring

1. & 2. Guest Lecture

Guo, J.

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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2013.10.30 Wed
  • Discovery Methods I.
  1. Hotspot probes (GRID)
  2. COMFA

1. Wang, S.

2. Xue, M.

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

2013.11.04 Mon
  • Discovery Methods II.
  1. Pharmacophores in drug design #1
  2. Pharmacophores in drug design #2

1. & 2. Guest Lecture

Jiang, L.

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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2013.11.06 Wed
  • Discovery Methods III.
  1. De novo design #1
  2. De novo design #2

1. & 2. Guest Lecture

Allen, W.

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

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2013.11.11 Mon
Quiz Prior Section IV

SECTION V: LEAD REFINEMENT

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

1. Zhao, P.

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

2013.11.13 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. Zong, Y.,

2. Zou, J.

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

2013.11.18 Mon
  • MM-GBSA case studies
  1. EGFR and mutants
  2. ErbB family selectivity

1. & 2.

Rizzo, R.

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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2013.11.20 Wed
  • Linear Response
  1. Intro to Linear Response (LR method)
  2. Inhibition of protein kinases (Extended LR method)

1. & 2. Guest Lecture

Zhou, Y.

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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2013.11.25 Mon
  • Quiz review 1
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2013.11.27 Wed
  • No Class: Thanksgiving
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2013.12.02 Mon
  • Quiz review 2
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2013.12.04 Wed
  • Quiz review 3
  1. Final Exam Study Guide Handout
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1. final_exam_study_guide

last day of class
2013.12.10 Tue
FINAL EXAM
Tuesday
8:30-11:00PM
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NOTE:

Unless otherwise noted the Final will be given in our regular class room.

It is the student’s responsibility to plan a class schedule that avoids exam conflicts and too many exams in the same day.

FINAL EXAM IS CUMULATIVE

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