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| #Introduction, history, irrational vs. rational | | #Introduction, history, irrational vs. rational |
| #Viral Target Examples | | #Viral Target Examples |
− | ||[http://ringo.ams.sunysb.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/Presentations/2014.08.27.ams535.rizzo.lect.001.pdf Rizzo, R.] | + | ||[https://ringo.ams.sunysb.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/Presentations/2014.08.27.ams535.rizzo.lect.001.pdf Rizzo, R.] |
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| 1. [http://ringo.ams.sunysb.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/References/Jorgensen009.pdf Jorgensen, W.L., The many roles of computation in drug discovery. ''Science'' '''2004''', ''303'', 1813-8] | | 1. [http://ringo.ams.sunysb.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/References/Jorgensen009.pdf Jorgensen, W.L., The many roles of computation in drug discovery. ''Science'' '''2004''', ''303'', 1813-8] |
Date
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Topic
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Speaker and Presentation
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Primary Reference
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Secondary Reference
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2014.08.25 Mon
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2014.08.27 Wed
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SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE
- Introduction, history, irrational vs. rational
- Viral Target Examples
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Rizzo, R.
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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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-
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2014.09.01 Mon
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-
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-
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-
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2014.09.03 Wed
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- Molecular structure, bonding, graphical representations
- Functionality, properties of organic molecules
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Rizzo, R.
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presentation
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-
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2014.09.08 Mon
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- Lipids, carbohydrates
- Nucleic acids, proteins
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Rizzo, R.
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presentation
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structures of the 20 amino acid side chains
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2014.09.10 Wed
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- Molecular Interactions and Recognition
- Electrostastics, VDW interactions, hydrophobic effect, molecular recognition (binding energy)
- Inhibitors types: allosteric, transition state, covalent vs non-covalent, selective, competitive
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Rizzo, R.
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presentation
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-
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2014.09.15 Mon
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- Intro. to Methods in 3-D Structure Determination
- Crystallography, NMR
- Structure Quality, PDB in detail
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Rizzo, R.
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presentation
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-
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2014.09.17 Wed
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Quiz Prior Section I
SECTION II: MOLECULAR MODELING
- All-atom Molecular Mechanics
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1. Guest Lecture
Zhou, Y.
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1. Mackerell, A. D., Jr., Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 2004, 25, 1584-604
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1. van Gunsteren, W. F.; et al., Biomolecular modeling: Goals, problems, perspectives. Angew. Chem. Int. Ed. Engl. 2006, 45, 4064-92
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2014.09.22 Mon
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- OPLS
- AMBER
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1. Belfon, K.
2. Hassan, M.
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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
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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
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2014.09.24 Wed
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- Water models (TIP3P, TIP4P, SPC)
- Condensed-phase calculations (DGhydration)
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1. Chiappone, S.
2. Chen, M.
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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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-
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2014.09.29 Mon
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- Generalized Born Surface Area (GBSA)
- Poisson-Boltzmann Surface Area (PBSA)
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1. Cortes, M.
2. Dinh, T.
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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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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
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2014.10.01 Wed
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Quiz Prior Section II
SECTION III: SAMPLING METHODS
- Small molecules, peptides, relative energy, minimization methods
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1. Guest Lecture
Rizzo, R.
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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
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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
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2014.10.06 Mon
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- Primary Sampling Methods for Computer Simulations
- Molecular dynamics (MD)
- Monte Carlo (MC)
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1. Levesque, B.
2. Pouryahya, M.
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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
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2. Metropolis, N.;et al., Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21, 1087-1092
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2014.10.08 Wed
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- Predicting Protein Structure I.
- Ab initio prediction (protein-folding)
- Example Trp-cage
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1. Elkin, R.
2. Elmes, M.
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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
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1-2. Daggett, V.; Fersht, A., The present view of the mechanism of protein folding. Nat. Rev. Mol. Cell Biol. 2003, 4, 497-502
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2014.10.13 Mon
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- Predicting Protein Structure II.
- Comparative (homology) modeling
- Case studies (CASP)
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1. Gu, Y.
2. Hu, K.
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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
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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
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2014.10.15 Wed
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- Enhanced Sampling Techniques
- Simulated annealing
- Ion Modeling
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1. & 2. Guest Lecture
Zhou, Y.
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1. Brunger, A. T.;Adams, P. D., Molecular dynamics applied to X-ray structure refinement. Acc. Chem. Res. 2002, 35, 404-12
2. Koca, J.; et al., Coordination number of zinc ions in the phosphotriesterase active site by molecular dynamics and quantum mechanics. J Comput. Chem. 2003, 24(3), 368-78
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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
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2014.10.20 Mon
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Quiz Prior Section III
SECTION IV: LEAD DISCOVERY
- Introduction to DOCK
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1. Kalra, J.
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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
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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
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2014.10.22 Wed
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- Test Sets (binding modes)
- Test Sets (virtual screening)
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1. & 2. Guest Lecture
Fochtman, B.
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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
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1. The CCDC/Astex Test Set
2. ZINC Website at UCSF, Shoichet group
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2014.10.27 Mon
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- Database Enrichment
- Footprint-based scoring
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1. & 2. Guest Lecture
Guo, J.
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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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-
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2014.10.29 Wed
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- Hotspot probes (GRID)
- COMFA
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1. Jones, G.
2. Sam-ang, P.
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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
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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
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2014.11.03 Mon
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- Pharmacophores in drug design #1
- Pharmacophores in drug design #2
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1. & 2. Guest Lecture
Jiang, L.
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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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-
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2014.11.05 Wed
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- De novo design #1
- De novo design #2
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1. & 2. Guest Lecture
Singleton, C.
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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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2014.11.10 Mon
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Quiz Prior Section IV
SECTION V: LEAD REFINEMENT
- Free Energy Perturbation (FEP)
- Thermolysin with two ligands
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1. Lam, K.
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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
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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
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2014.11.12 Wed
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- Thermodynamic integration
- MM-PB/GBSA
- Free energy calculation using TI
- Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods
|
1. Santos, R.
2. Shanbhogue, P.
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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
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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
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2014.11.17 Mon
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- EGFR and mutants
- ErbB family selectivity
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1. & 2. Guest Lecture
Rizzo, R.
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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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-
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2014.11.19 Wed
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- Intro to Linear Response (LR method)
- Inhibition of protein kinases (Extended LR method)
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1. Tian, C.
2. Zhang, B.
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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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-
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2014.11.24 Mon
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- Properties of Known Drugs and Protein Structure Prediction III.
- Molecular Scaffolds (frameworks) and functionality (side-chains)
- Lipinski Rule of Five
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2. Yin, X.
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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
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2. Lipinski, C. A., Chris Lipinski discusses life and chemistry after the Rule of Five. Drug. Discov. Today 2003, 8, 12-6
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2014.11.26 Wed
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-
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2014.12.01 Mon
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-
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-
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-
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2014.12.03 Wed
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- Final Exam Study Guide Handout
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-
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1. final_exam_study_guide
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last day of class
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