2009 AMS-535 Fall

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Announcements

  • Posted on 11/23/09 by Loretta
  1. The average for Quiz 4 is 19.9 and the standard deviation was 2.8.
  2. Anyone who is having trouble with the material should contact me directly, as the final is not expected to be any easier than the quizzes.
  • Posted on 11/10/09 by Loretta
  1. The average for Quiz 3 was 21.8 and the standard deviation was 1.9.
  2. Reminder: Quiz 4 is on Monday.
  • Posted on 10/27/09 by Loretta
  1. All slides relevant up to Quiz 3 have been posted.
  • Posted on 10/26/09 by Loretta
  1. I have emailed the last 4 presentations to everyone.
  2. Two of the four are currently available on this website; the others will follow soon.
  • Posted on 10/20/09 by Loretta
  1. Dr. Rizzo has posted all the presentations, since Quiz 1.
  • Posted on 10/19/09 by Loretta
  1. Quiz 2 has been graded and was returned today. The average was ~20.2.
  2. Although we went over the exam rather quickly today, if anything remains unclear, please feel free to contact either myself or Dr. Rizzo so that we can try to help you.
  3. Quiz 3 is next week. Presentations will be posted soon.
  • Posted on 10/07/09 by Loretta
  1. Dr. Rizzo has updated all the powerpoints for the presentations that we have had so far.
  2. Quiz 2 is on Monday!

Past Announcements

Example Quiz/Exam Questions from Prior Semesters

example.questions.pdf


Course Participants, Topics, References, and Schedule

Date
Topic
Speaker and Presentation
Primary Reference
Secondary Reference
2009.08.31 Mon
  • Organizational Meeting
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2009.09.02 Wed

SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE

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

1-2. Jorgensen, W. L., The many roles of computation in drug discovery. Science 2004, 303, 1813-8

1-2. Kuntz, I. D., Structure-based strategies for drug design and discovery. Science 1992, 257, 1078-1082

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2009.09.07 Mon
  • No Class: Labor Day
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2009.09.09 Wed
  • Chemistry Review
  1. Molecular structure, bonding, graphical representations
  2. Functionality, properties of organic molecules
Rizzo, R.
presentation
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2009.09.14 Mon
  • Biomolecular Structure
  1. Lipids, carbohydrates
  2. Nucleic acids, proteins
Rizzo, R.
presentation
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2009.09.16 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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2009.09.21 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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2009.09.23 Wed
Quiz Prior Section I


SECTION II: MOLECULAR MODELING

  • Classical Force Fields
  1. All-atom Molecular Mechanics

1. Goyal, R.

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

2009.09.28 Mon
  • No Class: Yom Kippur
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2009.09.29 Tues
Laufer Center Seminar Dr. Robert Jernigan
Chemistry 412 4 PM
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2009.09.30 Wed
  • Force Field Development
  1. OPLS
  2. AMBER

1. Adler, J.

2. Hauser, K.

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

2009.10.05 Mon
  • Explicit Solvent Models
  1. Water models (TIP3P, TIP4P, SPC)
  2. Condensed-phase calculations (DGhydration)

1. Barua, T.

2. Bhattacharjee, D.


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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2009.10.07 Wed
  • Continuum Solvent Models
  1. Generalized Born Surface Area (GBSA)
  2. Poisson-Boltzmann Surface Area (PBSA)

1. Cheglikov, A.

2. Falk, A.

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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2009.10.12 Mon
Quiz Prior Section II


SECTION III: SAMPLING METHODS

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

1. Gorgani, F.

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. NIH Online Molecular Modeling Guide

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

2009.10.14 Wed
  • Primary Sampling Methods for Computer Simulations
  1. Molecular dynamics (MD)
  2. Monte Carlo (MC)

1. Ascher, K.

2. Tang, T.

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

2009.10.19 Mon
  • Predicting Protein Structure I.
  1. Ab initio prediction (protein-folding)
  2. Example Trp-cage

1. Jiang, L.

2. Kirkup, C.

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

2009.10.21 Wed
  • Predicting Protein Structure II.
  1. Comparative (homology) modeling
  2. Case studies (CASP)

1. Lai, Eric

2. Lai, Z.

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

2009.10.26 Mon
  • Enhanced Sampling Techniques
  1. Simulated annealing
  2. Replica Exchange

1. Li, H.

2. Lin, P.

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

2. Sugita, Y.; Miyashita, N.; Yoda, T.; Ikeguchi, M.; Toyoshima, C., Structural Changes in the Cytoplasmic Domain of Phospholamban by Phosphorylation at Ser16: A Molecular Dynamics Study. Biochemistry 2006, 45, 11752-11761

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. Sugita, Y.; Okamoto, Y., Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 1999, 314, 141-151

2. Lei, H.; Duan, Y., Improved sampling methods for molecular simulation. Curr Opin Struct Biol 2007, 17, 187-91

2009.10.28 Wed
Quiz Prior Section III


SECTION IV: LEAD DISCOVERY

  • Docking I.
  1. Introduction to DOCK

Guest Lecture

1. 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

2009.11.02 Mon
  • Docking II.
  1. Test Sets (binding modes)
  2. Test Sets (virtual screening)

Guest Lecture

1. 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

2009.11.04 Wed
  • Discovery Methods I.
  1. Hotspot probes (GRID)
  2. COMFA

1. Murphy, P.

2. Neckles, C.

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

2009.11.09 Mon
  • Discovery Methods II.
  1. Pharmacaphores in drug design
  2. De nova design

1. Schwartz, K.

2. Shah, S.

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

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2009.11.11 Wed
  • Discovery Methods Applications
  1. Human Carbonic Anhydrase
  2. Estrogen Receptor

1. Son, M.

2. Song, B.


1. Gruneberg, S.; Stubbs, M. T.; Klebe, G., Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation. J. Med. Chem. 2002, 45, 3588-602

2. Waszkowycz, B.; Perkins, T. D. J.; Sykes, R. A.; Li, J., Large-scale virtual screening for discovering leads in the postgenomic era. IBM Systems Journal 2001, 40, 360-376

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

2009.11.16 Mon
Quiz Prior Section IV


SECTION V: LEAD REFINEMENT

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

1. Goyal, R.

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

2009.11.18 Wed
  • MM-PBSA, MM-GBSA
  1. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods
  2. MM-GBSA Case Study

Guest Lecture

1. Ramcharitar, R.

2. Balius, T.

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

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2009.11.23 Mon
  • MM-GBSA case studies
  1. HIVgp41
  2. influenza

1. Itaya, M.

2. Wang, T.

1. 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

2. Chachra, R.; Rizzo, R. C. Origins of Resistance Conferred by the R292K Neuraminidase Mutation via Molecular Dynamics and Free Energy Calculations. J. Chem. Theory Comput. 2008, 4, 1526-1540

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2009.11.25 Wed
  • No Class: Thanksgiving
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2009.11.30 Mon
  • Linear Response
  1. Intro to Linear Response (LR method)
  2. Inhibition of protein kinases (Extended LR method)

1. Watson, M.

2. Vo, 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

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2009.12.02 Wed
  • Industry Lecture
  1. Working in a Pharmaceutical Company

Guest Lecture

Dr. Elizabeth Buck

1. OSI Pharmaceuticals

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2009.12.07 Mon
  • Properties of Known Drugs
  1. Lipinski Rule of Five
  2. ADME prediction

1. Zamurrad, S.

2. Zang, Y.


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

2009.12.09 Wed
  • Properties of Known Drugs and Protein Structure Prediction III.
  1. Molecular Scaffolds (frameworks) and functionality (side-chains)
  2. Protein Design

1. Xu, X.

Guest Lecture

2. Au, L.

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. add reference

2009.12.14 Mon
FINAL EXAM
MON
2:15 - 4:45 PM
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NOTE:

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

FINAL EXAM IS CUMULATIVE

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