Difference between revisions of "2013 AMS-535 Fall"

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'''Current Announcements'''
*Posted on 11/19/2013 by Jeewoen
#Quiz 3 mean: 16.84 median: 17.03
#Quiz 4 mean: 15.67 median: 15.88
#On Mon. 11/25, Professor Rizzo will go over quiz problems in class. Don't forget to bring your all four quizzes.
*Posted on 10/08/2013 by Jeewoen
#Quiz 2 mean: 15.38 median: 16.5
#Email me if you have anything to claim (~10/15).
*Posted on 09/26/2013 by Jeewoen
Future quizzes are going to be based on primary papers and classes(presentations).
You are not expected to know every detail in papers but you have to understand at least main ideas, methods and results.
*Posted on 09/23/2013 by Jeewoen
#Quiz 1 mean: 18.6325 median: 20.25
#Email me first, if you have anything to claim (~9/27).
*Posted on 09/10/2013 by Jeewoen
#The presentation schedule is now updated. Please check your stonybrook email for the new announcement.
*Posted on 08/26/2013 by Jeewoen
#PubMed: http://www.ncbi.nlm.nih.gov/pubmed/ You can search articles and find references on this website.
*Posted on 08/22/2013 by Jeewoen
#This site will be updated soon for the fall 2013 class.
*Posted on 08/22/2013 by Jeewoen
#The fall [http://www.stonybrook.edu/registrar/Fall%202013%20Calendars/GRAD%201138%20073113.pdf class schedule] and [http://www.stonybrook.edu/registrar/Fall2013Exams/1138%20Fall%202013%20final%20exams%20rev%20(3).pdf Final Exam schedule] can be found on the school website.
#'''"It is the student’s responsibility to plan a class schedule that avoids exam conflicts and too many exams in the same day."''' (as stated in the university final exam schedule sheet)
== Example Quiz/Exam Questions from Prior Semesters ==
* [http://ringo.ams.sunysb.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/Presentations/ams535.example.questions.pdf example.questions.pdf]
== Course Participants, Topics, References, and Schedule ==
* '''Please note that a doctors excuse will be required if you miss a test or your scheduled oral presentation date because of illness.'''
[[Announcements: Fall 2013]]
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Latest revision as of 11:57, 7 April 2014

DOCK graphic egfr2.png

Announcements: Fall 2013

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


  • 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

2013.09.02 Mon
  • No Class: Labor Day
2013.09.04 Wed
  • Chemistry Review
  1. Molecular structure, bonding, graphical representations
  2. Functionality, properties of organic molecules
Rizzo, R.
2013.09.09 Mon
  • Biomolecular Structure
  1. Lipids, carbohydrates
  2. Nucleic acids, proteins
Rizzo, R.
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.
2013.09.16 Mon
  • Intro. to Methods in 3-D Structure Determination
  1. Crystallography, NMR
  2. Structure Quality, PDB in detail
Rizzo, R.
2013.09.18 Wed
Quiz Prior Section I


  • 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

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

2013.10.02 Wed
Quiz Prior Section II


  • 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


  • 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.

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

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

2013.11.11 Mon
Quiz Prior Section IV


  • 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
  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

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

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

1. final_exam_study_guide

last day of class
2013.12.10 Tue

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.