Difference between revisions of "2020 AMS-535 Fall"

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(1) Pre-recorded oral presentations (25%)
 
(1) Pre-recorded oral presentations (25%)
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::each student will pre-record 2 ZOOM presentations based on papers in the schedule below <br>
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::Student will pre-record 2 ZOOM presentations based on papers in the schedule below <br>
 
(2) Class discussion (30%)
 
(2) Class discussion (30%)
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::at scheduled class times, students will go into ZOOM breakout rooms <br>
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::At scheduled class times students will be assigned into ZOOM breakout rooms and asked to discuss the papers they have read and the presentations they have watched <br>
 
(3) Take home quizzes (45%)
 
(3) Take home quizzes (45%)
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::5 quizzes will be given and the lowest quiz grade will be dropped
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::Five take home quizzes will be assigned based on the 5 major sections of the course and the lowest quiz grade will be dropped
 
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Revision as of 11:44, 22 August 2020

Please see http://ringo.ams.sunysb.edu/~rizzo for Rizzo Group Homepage


Instructor Dr. Robert C. Rizzo [631-632-8519, rizzorc -at- gmail.com]

Dr. Guilherme Duarte Ramos Matos [631-632-8519, guilherme dot duarteramosmatos -at- stonybrook dot edu]

TA John Bickel [631-632-8519, john dot bickel -at- stonybrook dot edu]
Course No. AMS-535 / CHE-535
Location/Time Online, Monday and Wednesday 2:40PM - 4:00PM
Office Hours Anytime by appointment, Math Tower 3-129
Grading Grades will be based on the quality of:

(1) Pre-recorded oral presentations (25%)

Student will pre-record 2 ZOOM presentations based on papers in the schedule below

(2) Class discussion (30%)

At scheduled class times students will be assigned into ZOOM breakout rooms and asked to discuss the papers they have read and the presentations they have watched

(3) Take home quizzes (45%)

Five take home quizzes will be assigned based on the 5 major sections of the course and the lowest quiz grade will be dropped



Online Syllabus Notes

As a result of the COVID-19 outbreak this course is being offered online. This is a mixed course meaning that there will be both synchronous and asynchronous aspects. Note that course grading criteria has been modified from previous years (see grading breakdown above). Other details for this semester are as follows:

  • We will to hold class at the regularly scheduled time (M/W 2:40-4:00PM) however this will be done online via ZOOM.
  • The first 5 lectures are to help put everyone on an even footing with regards to background material and will be given by the Instructors at the regularly scheduled class time and also made available on the class website.
  • The rest of the classes will be devoted to Discussion of papers read by all course participants (2 per class) for which everyone will also have watched a short oral presentation prior to coming to class.
  • During the Discussion sessions (ZOOM breakout rooms) the Instructors will ask participants to explain details of the papers they have read which will form the basis of the "Discussion" part of their grade. Thus, it is important that everyone attend all of the synchronous classes.
  • If a student is unable to attend an online class due to internet or other issues then they will instead submit a one page Paper Summary Sheet answering questions about the papers they have read. The "Paper Summary Sheets" will form the basis of the "Discussion" part of their grade for any synchronous classes that were missed.
  • Students will pre-record 2 different ZOOM presentations based on 2 different papers from the schedule shown below.
  • Students will email their pre-recorded presentations to the course Instructors by Friday at 5PM before the week in which their presentations will be discussed.
  • Course participants will watch the student presentations before the class in which they are to be discussed.
  • Course participants will score each student presentation using a Presentation Assessment Sheet which will be emailed to ALL Instructors prior to the class in which the presentation is being discussed.
  • All class correspondence should be addressed to all course Instructors.


Recording Your Oral Presentations Using Zoom: It is very straightforward to create a video of yourself giving a PPT presentation using Zoom:

  • Download the Zoom app ( https://it.stonybrook.edu/services/zoom )
  • Open the Zoom app
  • Create a new Zoom meeting with only yourself (make sure audio and video are turned on)
  • Share your screen
  • Open your presentation in PPT and put in presentation mode
  • Start recording and give a short test presentation to make sure that everything is working smoothly (use mouse as necessary to highlight specific regions of your slides)
  • Stop recording and quit the meeting
  • Open the newly created video (using QuickTime or some other video player) to make sure that your test presentation has both audio and video and looks good
  • Follow the above steps to create your "full-length" video presentation (videos should not exceed 20 minutes)
  • Email your video to the Instructors who will make it available to the class (please name your Zoom video Lastname.mp4)


Oral Presentation Guidelines: Recorded Talks should be presented in PPT format and be between 20 and 25 minutes long. The purpose of your talks is for you to clearly and concisely present the papers assigned to you in the schedule below. Talks should be arranged in the following order:

  • Introduction/Background (include biological relevance)
  • Specifics of Your System
  • Computational Details (theory)
  • Computational Details (system setup)
  • Results and Discussion (include a critical interpretation of your results)
  • Conclusions
  • Future
  • Acknowledgments

Video Presentations of Class Projects Must be Emailed to Guilherme by 2:30PM (please name files as "Lastname.mp4")
Project Analysis/Troubleshooting

Each course participant will watch and evaluate 3 Presentations (~ 20 minutes each) based on the Reviewer Assignment schedule below and submit a Presentation Assessment Sheet for each Video (due 1 week from today). Instructors will evaluate 3-4 presentations each.


Date
Topic
Speaker and Presentation
Primary Reference
Secondary Reference
2019.08.24 Mon
  • Organizational Meeting
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2019.08.26 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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2019.08.31 Mon
  • Chemistry Review
1. Molecular structure, bonding, graphical representations
2. Functionality, properties of organic molecules
Rizzo, R.
presentation
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2019.09.02 Wed
  • Biomolecular Structure
1. Lipids, carbohydrates
2. Nucleic acids, proteins
Rizzo, R.
presentation
 structures of the 20 amino acid side chains
2019.09.07 Mon
  • No Class: Labor Day
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2019.09.09 Wed
  • Molecular Interactions and Recognition
1. Electrostatics, 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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2019.09.14 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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Take home QUIZ for Section 1 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
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2019.09.16 Wed

SECTION II: MOLECULAR MODELING

  • Classical Force Fields
1. All-atom Molecular Mechanics
2. OPLS

1. last, first

2. last, first

1. Mackerell, A. D., Jr., Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 2004, 25, 1584-604

2. 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

1. van Gunsteren, W. F.; et al., Biomolecular modeling: Goals, problems, perspectives. Angew. Chem. Int. Ed. Engl. 2006, 45, 4064-92

2. 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

2019.09.21 Mon
  • Classical Force Fields
1. AMBER
  • Explicit Solvent Models
2. Water models (TIP3P, TIP4P, SPC)

1. last, first

2. last, first

1. 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

2. Jorgensen, W. L.; et al., Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926-935

1. 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

2019.09.23 Wed
  • Explicit Solvent Models
1. Condensed-phase calculations (DGhydration)
  • Continuum Solvent Models
2. Generalized Born Surface Area (GBSA)

1. last, first

2. last, first

1. Jorgensen, W. L.; et al., Monte Carlo Simulation of Differences in Free Energies of Hydration. J. Chem. Phys. 1985, 83, 3050-3054

2. Still, W. C.; et al., Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. J. Am. Chem. Soc 1990, 112, 6127-6129

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2019.09.28 Mon
  • Continuum Solvent Models
1. Poisson-Boltzmann Surface Area (PBSA)
2. Accuracy of partial atomic changes for GBSA and PBSA

1. last, first

2. last, first

1. Sitkoff, D.; et al., Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models. J. Phys. Chem. 1994, 98, 1978-1988

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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Take home QUIZ for Section 2 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
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2019.09.30 Wed

SECTION III: SAMPLING METHODS

  • Molecular Conformations
1. Small molecules, peptides, relative energy, minimization methods
  • Sampling Methods for Large Simulations
2. Molecular dynamics (MD)

1. last, first

2. last, first

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

2. Karplus, M.; Petsko, G. A., Molecular dynamics simulations in biology. Nature 1990, 347, 631-9

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

2019.10.05 Mon
  • Sampling Methods for Large Simulations
1. Monte Carlo (MC)
  • Predicting Protein Structure
2. Ab initio structure prediction (protein-folding)

1. last, first

2. last, first

1. Metropolis Monte Carlo Simulation Tutorial, LearningFromTheWeb.net, Accessed Oct 2008, Luke, B.

1. Jorgensen, W. L.; TiradoRives, J., Monte Carlo vs Molecular Dynamics for Conformational Sampling. J. Phys. Chem. 1996, 100,14508-14513

2. Dill, K. A.; Chan, H. S., From Levinthal to pathways to funnels. Nat. Struct. Biol. 1997, 4, 10-19

1. Metropolis, N.;et al., Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21, 1087-1092

2019.10.07 Wed
  • Predicting Protein Structure
1. Example Trp-cage
2. Comparative (homology) modeling

1. last, first

2. last, first

1. Simmerling, C.;et al., All-atom structure prediction and folding simulations of a stable protein. J. Am. Chem. Soc. 2002, 124,11258-9

2. Marti-Renom, M. A.; et al., Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 2000,29,291-325

1. Daggett, V.; Fersht, A., The present view of the mechanism of protein folding. Nat. Rev. Mol. Cell Biol. 2003, 4, 497-502

2. Fiser, A.; et al., Evolution and physics in comparative protein structure modeling. Acc. Chem. Res. 2002, 35, 413-21

2019.10.12 Mon
  • Predicting Protein Structure
1. Case studies (CASP)
2. Accelerated MD for Blind Protein Prediction

1. last, first

2. last, first

1. Moult, J., A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 2005,15, 285-9

2. Perez, A.; et al., Blind protein structure prediction using accelerated free-energy simulations. Sci. Adv. 2016, 2

1. Kryshtafovych, A.; et al., Progress over the first decade of CASP experiments. Proteins 2005, 61 Suppl 7, 225-36

2019.10.14 Wed
  • Predicting Protein Structure
1. MD x-ray refinement
2. Protein Design

1. last, first

2. last, first

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

1. Kuhlman, A. T.;et al, Design of a Novel Globular Protein Fold with Atomic-Level Accuracy. Science 2003, 302, 1364-1368

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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Take home QUIZ for Section 3 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
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2019.10.19 Mon

SECTION IV: LEAD DISCOVERY

  • Docking
1. Introduction to DOCK
2. Test Sets (pose reproduction)

1. last, first

2. last, first

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

2. Mukherjee, S.; et al., Docking Validation Resources: Protein Family and Ligand Flexibility Experiments. J. Chem. Info. Model. 2010, 50, 1986-2000

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

2. The CCDC/Astex Test Set

2019.10.21 Wed
  • Docking
1. Test Sets (virtual screening)
2. Test Sets (database enrichment)

1. last, first

2. last, first


1. 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

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

1. ZINC Website at UCSF, Shoichet group

2019.10.26 Mon
  • Docking
1. Footprint-based scoring
  • Discovery Methods
2. Hotspot probes (GRID)

1. last, first

2. last, first

1. Balius, T.E.; et al., Implementation and Evaluation of a Docking-Rescoring Method Using Molecular Footprint Comparisons. J. Comput. Chem. 2011, 32, 2273-2289.

1. Goodford, P. J., A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-57

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2019.10.28 Wed
  • Discovery Methods
1. COMFA
2 Pharmacophores

1. last, first

2. last, first

1. Kubinyi, H., Comparative molecular field analysis (CoMFA). Encyclopedia of Computational Chemistry, Databases and Expert Systems Section, John Wiley & Sons, Ltd. 1998

2. Chang, C.; et al., Pharmacophore-based discovery of ligands for drug transporters. Advanced Drug Delivery Reviews 2006, 58, 1431-1450

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

2019.11.02 Mon
  • Discovery Methods.
1. Pharmacophores
2. De novo design

1. last, first

2. last, first

1. Alvarez, J.; et al., Pharmacophore-Based Molecular Docking to Account for Ligand Flexibility. Proteins 2003, 51, 172-188

2. Cheron, N.; et al., OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands. J. Med. Chem. 2016, 59, 4171-4188

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2019.11.04 Wed
  • Discovery Methods
1. De novo design
2. Genetic Algorithm

1. last, first

2. last, first

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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Take home QUIZ for Section 4 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
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2019.11.09 Mon

SECTION V: LEAD REFINEMENT

  • Free Energy Methods
1. Thermolysin with two ligands (FEP)
2. Fatty acid synthase I ligands (TI)

1. last, first

2. last, first

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

2. 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

1&2. Jorgensen, W. L., Free Energy Calculations: A Breakthrough for Modeling Organic Chemistry in Solution. Accounts Chem. Res. 1989, 22, 184-189

1&2. Kollman, P., Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93, 2395-2417

2. 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

2019.11.11 Wed
  • MM-PB/GBSA
1. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods
  • MM-GBSA case studies
2. EGFR and mutants

1. last, first

2. last, first


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

2019.11.16 Mon
  • MM-GBSA case studies
1. ErbB family selectivity
  • Linear Response
2. Intro to Linear Response (LR method)

1. last, first

2. last, first


1. Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406

2. 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

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2019.11.18 Wed
  • Linear Response
1. Inhibition of protein kinases (Extended LR method)
  • Properties of Known Drugs
2. Molecular Scaffolds (frameworks) and functionality (side-chains

1. last, first

2. last, first


1. 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

2. Bemis, G. W.; Murcko, M. A., The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 1996, 39, 2887-93

2. Bemis, G. W.; Murcko, M. A., Properties of known drugs. 2. Side chains. J. Med. Chem. 1999, 42, 5095-9

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2019.11.23 Mon
  • No Class: Thanksgiving
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2019.11.23 Wed
  • No Class: Thanksgiving
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2019.11.30 Mon
  • Properties of Known Drugs
1. Lipinski Rule of Five
2 ADME Prediction

1. last, first

2. last, first

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

2019.12.02 Wed
  • TBD
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2019.12.07 Mon
  • TBD
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Take home QUIZ for Section 5 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
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  • Final Exam
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 No Final Exam in AMS-535/CHE-535 for Fall 2020
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