2010 AMS-535 Fall

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Past Announcements

Current Announcements

  • Posted on 10/18/2010 by Lingling
  1. The average score for quiz II is 17.29 out of 24. We are eliminating question 6 from the total score as most people get confused by this question. If you need any help with the problems in the quiz, feel free to make an appointment with me and discuss about them.
  2. Students who presented should email me the three quiz questions (one simple, one medium and one hard).
  • Posted on 10/19/2010 by Lingling
  1. The notes for 10/11 to 10/18's classes have been posted. The next quiz is next Wednesday. Please be prepared.
  • Posted on 10/19/2010 by Lingling
  1. The notes for 10/20's class have been posted. Today's guest lecture will be posted later today. The 3rd quiz will cover all the presentation in this section, including the guest lecture.

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
2010.08.30 Mon
  • Organizational Meeting
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2010.09.01 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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2010.09.06 Mon
  • No Class: Labor Day
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2010.09.08 Wed
  • Chemistry Review
  1. Molecular structure, bonding, graphical representations
  2. Functionality, properties of organic molecules
Class ends at 5:00PM
 Rizzo, R.
presentation
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2010.09.13 Mon
  • Biomolecular Structure
  1. Lipids, carbohydrates
  2. Nucleic acids, proteins
Rizzo, R.
presentation
 structures of the 20 amino acid side chains
2010.09.15 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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2010.09.20 Mon Class in diff location and time
 *CHE-607 Modern Drug Design and Discovery: Computational Biology Lectures Rizzo, R.
NOTE:

For today only we will merge with Professor Ojima's "Modern Drug Design and Discovery" class.

CLASS ROOM CHANGE and TIME CHANGE:

Chemistry Department Room 410, 3:20PM - 5:20PM
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2010.09.22 Wed
  • Intro. to Methods in 3-D Structure Determination
  1. Crystallography, NMR
  2. Structure Quality, PDB in detail
Rizzo, R.
presentation
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2010.09.27 Mon
Quiz Prior Section I

SECTION II: MOLECULAR MODELING

  • Classical Force Fields
  1. All-atom Molecular Mechanics

Guest Lecture

Balius, T.

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

2010.09.29 Wed
  • Force Field Development
  1. OPLS
  2. AMBER

1. Akter, R.

2. Cao, Y.

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

2010.10.04 Mon
  • Explicit Solvent Models
  1. Water models (TIP3P, TIP4P, SPC)
  2. Condensed-phase calculations (DGhydration)

1. Chen, J.

2. Conte, 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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2010.10.06 Wed
  • Continuum Solvent Models
  1. Generalized Born Surface Area (GBSA)
  2. Poisson-Boltzmann Surface Area (PBSA)

1. Efaplomatides, C.

2. Fochtman, 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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2010.10.11 Mon
Quiz Prior Section II

SECTION III: SAMPLING METHODS

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

1. Gardin, J.

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

2010.10.13 Wed
  • Primary Sampling Methods for Computer Simulations
  1. Molecular dynamics (MD)
  2. Monte Carlo (MC)

1. Maringano, D.

2. Yao, Y

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

2010.10.18 Mon
  • Predicting Protein Structure I.
  1. Ab initio prediction (protein-folding)
  2. Example Trp-cage

1. Grinshpun, B.

2. Hancewicz, J.

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

2010.10.20 Wed
  • Predicting Protein Structure II.
  1. Comparative (homology) modeling
  2. Case studies (CASP)

1. Jee, J.

2. Jin, X.

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

2010.10.25 Mon
  • Enhanced Sampling Techniques
  1. Simulated annealing
  2. Protein Design

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. Street, A. G.; Mayo, S. L., Computational protein design. Structure. 1999, 7, 105-9

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. Lippow, S. M.; Tidor, B., Progress in computational protein design. Curr. Opin. Biotechnol. 2007, 18, 305-311

2010.10.27 Wed
Quiz Prior Section III


SECTION IV: LEAD DISCOVERY

  • Docking I.
  1. Introduction to DOCK

Guest Lecture

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

2010.11.01 Mon
  • Discovery Methods I.
  1. Hotspot probes (GRID)
  2. COMFA

1. Lee, S.

2. Lei, L.

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

2010.11.03 Wed
  • Discovery Methods II.
  1. Pharmacaphores in drug design
  2. De nova design

1. Ashiru-Balogun, J.

2. Li, M.

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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2010.11.08 Mon
  • Docking II.
  1. Test Sets (binding modes)
  2. Test Sets (virtual screening)

Guest Lecture

Mukherjee, S.

1. Mukherjee, S.; et al., Docking Validation Resources: Protein Family and Ligand Flexibility Experiments. J. Chem. Info. Model. 2010, in press

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

2010.11.10 Wed
  • Docking III.
  1. Enrichment and Rescoring

Guest Lecture Balius, T.

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

2. Deng, Z; et al., Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions. J. Med. Chem. 2004, 47, 337-344

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2010.11.15 Mon
Quiz Prior Section IV


SECTION V: LEAD REFINEMENT

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

1. Li, Z.

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

2010.11.17 Wed
  • Thermodynamic Integration, MM-PB/GBSA
  1. H5N1 Avian influenza N1-PVR
  2. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods

1. Liao, J.

2. Liu, J.

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

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

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2010.11.22 Mon
  • Case study
  1. TI and MM-PBSA
  2. TI and MM-GBSA

1. Liu, Y.

2. Messina, D.

1. Kollman, P. A.; Gouda, H.;Kuntz, I. D.; Case, D. A.; Free Energy Calculations for Theophylline Binding to an RNA Aptamer: Comparison of MM-PBSA and Thermodynamic Integration Methods. Biopolymers. 2003, 68, 16-34

2. 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

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2010.11.24 Wed
  • No Class: Following a Friday schedule
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2010.11.29 Mon
  • MM-GBSA case studies
  1. EGFR
  2. HIVgp41


1. Spaqnuolo, L.

2. Van Wart, T.

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

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

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2010.12.01 Wed
  • Linear Response
  1. Intro to Linear Response (LR method)
  2. Inhibition of protein kinases (Extended LR method)

1. Yang, R.

2. Hambardzhieva, 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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2010.12.06 Mon
  • Properties of Known Drugs
  1. Lipinski Rule of Five
  2. ADME prediction

1. Yerramilli, V.

2. Yu, W.


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

2010.12.08 Wed
  • Properties of Known Drugs and Protein Structure Prediction III.
  1. Molecular Scaffolds (frameworks) and functionality (side-chains)
  2. TBA

1. TBA


2. TBA.

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. TBA

2. TBA

2010.12.13 Mon
FINAL EXAM
MON
TIME TBA
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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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