2024 AMS-535 Fall

Please see https://ringo.ams.stonybrook.edu/~rizzo for Rizzo Group Homepage

Instructors	Dr. Robert C. Rizzo [631-632-8519, rizzorc -at- gmail.com] Brock Boysan [631-632-8519, brock dot boysan -at- stonybrook dot edu]
Course No.	AMS-535 / CHE-535
Location/Time	FREY HALL 326 WESTCAMPUS, MoWe 2:00PM - 3:20PM
Office Hours	Anytime by appointment, Math Tower 3-129
Grading	Grades will be based on the quality of: (1) Oral presentations (25%) Student will give 1-2 oral presentations (in-person) based on papers assigned from the schedule below. (2) Class discussion (25%) Before each class, course participants will read the papers that are to be orally presented and come prepared to ask thoughtful questions and participate in scientific discussion about the topics being presented. (3) Quizzes (50%) Five in class quizzes will be assigned based on the 5 major sections of the course and the lowest quiz grade will be dropped.

GENERAL INFORMATION: AMS-535 provides an introduction to the field of computational structure-based drug design. The course aims to foster collaborative learning and will consist of presentations by instructors, course participants, and guest lecturers arranged in five major sections outlined below. Presentations should aim to summarize key papers, theory, and application of computational methods relevant to computational drug design. Grade will be based on oral presentations, class discussion/attendance/participation, and quizzes.

Learning Objectives

• (1) Become informed about the field of computational structure-based drug design and the pros and cons of its methods.
• (2) Dissect seminal theory and application papers relevant to computational drug design.
• (3) Gain practice in giving an in-depth oral powerpoint presentation on computational drug design.
• (4) Read, participate in discussion, and be tested across five key subject areas:
  • (i) Drug Discovery and Biomolecular Structure:
    Drug Discovery, Chemistry Review, Proteins, Carbohydrates, Nucleic acids
    Molecular Interactions and Recognition, Experimental Techniques for Elucidating Structure
  • (ii) Molecular Modeling:
    Classical Force Fields (Molecular Mechanics),
    Solvent Models, Condensed-phase Calculations, Parameter Development
  • (iii) Sampling Methods:
    Conformational Space, Molecular Dynamics (MD), Metropolis Monte Carlo (MC)
    Sampling Techniques, Predicting Protein Structure, Protein Folding
  • (iv) Lead Discovery:
    Docking as a Lead Generation Tool, Docking Algorithms
    Discovery Methods I, Discovery Methods II, Applications
  • (v) Lead Refinement:
    Free Energy Perturbation (FEP), Linear Response (LR), Extended Linear Response (ELR)
    MM-PBSA, MM-GBSA, Properties of Known Drugs, Property Prediction

LITERATURE DISCLAIMER: Hyperlinks and manuscripts accessed through Stony Brook University's electronic journal subscriptions are provided below for educational purposes only.

PRESENTATION DISCLAIMER: Presentations may contain slides from a variety of online sources for educational and illustrative purposes only, and use here does not imply that the presenter is claiming that the contents are their own original work or research.

Syllabus Notes

General Information:

• This is an in-person course. There is no online section.
• Note that course grading criteria has been modified from previous years (see grading breakdown above).
• 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.
• All class correspondence should be addressed to ALL course Instructors.

Oral Presentations:

• Each participant will give 1-2 oral presentations (depending on the class size).
• In-person presentations should be formal (as if at a scientific meeting or job talk), presented in PPT format, and be 20-25 minutes long.
• All presentations will be recorded and posted on the course website.
• References should occur at the bottom of each slide when necessary.
• Presentations should be based mostly on the primary references however secondary references and other sources may be required to make some presentations complete.
• It is the responsibility of each presenter to email a PPT file of their talk by Friday at 5PM before the week in which their talk is being discussed.
• In general, talks will likely be arranged in the following order: (1) Introduction/Background (include biological relevance if applicable), (2) Specifics of the System or General Problem, (3) Computational Methods (theory) and Details (system setup) being used, (4) Results and Discussion (critical interpretation of results and any problems/challenges), (5) Conclusions/Future, and (6) Acknowledgments.

Class Discussion:

• A sizable portion of the course grade is based on participating in Class Discussion. Thus, it is important that everyone attend all of the classes and participate in scientific discussion to receive full credit.
• If a student is unable to attend a specific class for reasons beyond their control they will instead be asked to email the instructor(s) AND submit a one page Paper Summary Sheet answering questions about the papers that were discussed on the day that they missed. The "Paper Summary Sheets" will form the basis of the "Class Discussion" part of their grade for any classes that were missed.
• If a student misses a class they will have 24 hours to submit their Paper Summary Sheets. Late Paper Summary Sheets will not be accepted.

Quizzes:

• Five quizzes will be used to assess student understanding of the course material.
• The quiz format is in-class, closed book.
• Answers to quiz questions should integrate topics, concepts, and outcomes of the different papers covered for the section being tested.
• Students are expected to work alone and do their own work. Representing another person's work as your own is always wrong. The Instructors are required to report any and all suspected instances of academic dishonesty to the students Graduate Program Director.

Class Schedule

Date	Topic	Speaker and Presentation	Primary Reference	Secondary Reference
2023.08.28 Mon	Organizational Meeting	Rizzo, R	Course introduction and format. Go over Syllabus. Course participant background and introductions.	-
-	-	-	START SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE	-
2023.08.30 Wed	Drug Discovery 1. Introduction, history, irrational vs. rational 2. Viral Target Examples	Rizzo, R. lecture slides pdf	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	Rizzo, R. prior recorded lecture 1 mp4
2023.09.04 Mon	-	-	NO CLASS - LABOR DAY	-
2023.09.06 Wed	Chemistry Review 1. Molecular structure, bonding, graphical representations 2. Functionality, properties of organic molecules	Rizzo, R. lecture slides pdf	in class lecture	Rizzo, R. prior recorded lecture 2 mp4
2023.09.11 Mon	Biomolecular Structure 1. Lipids, carbohydrates 2. Nucleic acids, proteins	Rizzo, R. lecture slides pdf	in class lecture	Rizzo, R. prior recorded lecture 3 mp4 Structures/definitions of the 20 amino acid side chains used for this class pdf
2023.09.13 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. lecture slides pdf	in class lecture	Rizzo, R. prior recorded lecture 4 mp4
2023.09.18 Mon	Intro. to Methods in 3-D Structure Determination 1. Crystallography, NMR 2. Structure Quality, PDB in detail	Rizzo, R. lecture slides pdf	in class lecture	Rizzo, R. prior recorded lecture 5 mp4
-	-	-	START SECTION II: MOLECULAR MODELING after QUIZ #1	-
2023.09.20 Wed Quiz #1	Classical Force Fields 1. All-atom Molecular Mechanics	1. Last, First mp4 pdf	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
2023.09.25 Mon	Classical Force Fields 1. OPLS 2. AMBER	1. Last, First mp4 pdf 2. Last, First mp4 pdf	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
2023.09.27 Wed	Explicit Solvent Models 1. Water models (TIP3P, TIP4P, SPC) 2. Condensed-phase calculations (DGhydration)	1. Last, First mp4 pdf 2. Last, First mp4 pdf	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	2. Thermodynamic Cycle as Drawn By Dr. Rizzo
2023.10.02 Mon	Continuum Solvent Models 1. Generalized Born Surface Area (GBSA) 2. Poisson-Boltzmann Surface Area (PBSA)	1. Last, First mp4 pdf 2. Last, First mp4 pdf	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	-
-	-	-	'START SECTION III: SAMPLING METHODS after QUIZ #2	-
2023.10.04 Wed Quiz #2	Molecular Conformations 1. Small molecules, peptides, relative energy, minimization methods	1. Last, First mp4 pdf	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
2023.10.09 Mon	-	-	NO CLASS - FALL BREAK	-
2023.10.11 Wed	Sampling Methods for Large Simulations 1. Molecular dynamics (MD) 2. Monte Carlo (MC)	1. Last, First mp4pdf 2. Last, First mp4pdf	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
2023.10.16 Mon	Predicting Protein Structure 1. Ab initio structure prediction (protein-folding) 2. Example Trp-cage	1. Last, First mp4 pdf 2. Last, First mp4pdf	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	2. 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
2023.10.18 Wed	Predicting Protein Structure 1. Comparative (homology) modeling 2. Neural Network	1. Last, First mp4 pdf 2. Last, First mp4pdf	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. Senior, A.;et al, Improved protein structure prediction using potentials from deep learning. Nature 2020, 577, 1-5	-
2023.10.23 Mon	Predicting Protein Structure 1. Accelerated MD for Blind Protein Prediction 2. Meso-Scale MD (Markov State Models)	1. Last, First mp4pdf 2. Last, First mp4pdf	1. Perez, A.; et al., Blind protein structure prediction using accelerated free-energy simulations. Sci. Adv. 2016, 2 2. Durrant, J.;et al, Mesoscale All-Atom Influenza Virus Simulations Suggest New Substrate Binding Mechanism. ACS Central Science 2020, 6, 189-196	2. Husic, B.; et al., Markov State Models: From an Art to a Science. JACS 2018, 140, 2386-2396
-	-	-	START SECTION IV: LEAD DISCOVERY after QUIZ #3	-
2023.10.25 Wed Quiz #3	SECTION IV: LEAD DISCOVERY Docking 1. Introduction to DOCK	1. Last, First mp4 pdf	1. Allen, W. J.; et al., DOCK 6: Impact of New Features and Current Docking Performance. Journal of computational chemistry 2015, 36, 1132-1156.	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
2023.10.30 Mon	Docking 1. Test Sets (database enrichment) 2. Test Sets (virtual screening)	1. Last, First mp4 pdf 2.Last, First mp4 pdf]	1. Mysinger, M.; et al., Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. Journal of medicinal chemistry 2012, 55, 6582-94 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	2. ZINC Website at UCSF, Shoichet group
2023.11.01 Wed	Docking 1. Footprint-based scoring Discovery Methods 2. Hotspot probes (GRID)	1. Last, First mp4 pdf 2. Last, First mp4pdf	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. 2. Goodford, P. J., A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-57	-
2023.11.06 Mon	Discovery Methods 1. COMFA 2. Pharmacophores	1. Last, First mp4pdf 2. Last, First mp4pdf	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
2023.11.08 Wed	Discovery Methods. 1. Pharmacophores 2. De novo design	1. Last, First mp4pdf 2. Last, First mp4pdf	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	-
2023.11.13 Mon	Discovery Methods 1. De novo design 2. Genetic Algorithm	1. Last, First mp4pdf 2. Last, First mp4pdf	1. Jorgensen, W.; et al., Efficient drug lead discovery and optimization. Acc. of Chem. Research 2009, 42 (6), 724-733 2. Prentis, L and Singleton, C et al.; A molecular evolution algorithm for ligand design in DOCK J Comp Chem 2022, 43 (29), 1942-1963	-
-	-	-	START SECTION V: LEAD REFINEMENT after QUIZ #4	-
2023.11.15 Wed Quiz #4	Free Energy Methods 1. Thermolysin with two ligands (FEP)	1. Last, First mp4pdf	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. Bartlett, P. A.; Marlowe, C. K., Evaluation of Intrinsic Binding Energy from a Hydrogen Bonding Group in an Enzyme Inhibitor. Science. 1987, 235, 569-571 1. Tronrud, D.E.; Holden, H.M.; Matthews, B.W.; Structures of Two Thermolysin-Inhibitor Complexes That Differ by a Single Hydrogen Bond. Science 1987, 235, 571-573 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
2023.11.20 Mon	Thermodynamic Integration 1. Fatty acid synthase I ligands MM-PB/GBSA 2. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods	1. Last, First mp4pdf 2. Last, First mp4pdf	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
2021.11.22 Wed	-	-	NO CLASS: THANKSGIVING	-
2023.11.27 Mon	MM-GBSA case studies" 1. EGFR and mutants 2. ErbB family selectivity	1. Last, First mp4pdf 2. Last, First mp4pdf	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	-
2023.11.29 Wed	Linear Response 1. Intro to Linear Response (LR method) 2. Inhibition of protein kinases (Extended LR method)	1. Last, First mp4pdf 2. Last, First mp4pdf	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	-
2023.12.04 Mon	Properties of Known Drugs 1. Molecular Scaffolds (frameworks) and functionality (side-chains) 2. Lipinski Rule of Five	1. Last, First mp4pdf 2. Last, First mp4pdf	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. 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. Lipinski, C. A., Chris Lipinski discusses life and chemistry after the Rule of Five. Drug. Discov. Today 2003, 8, 12-6
2023.12.06 Wed	Properties of Known Drugs 1. ADME Prediction 2. QED	1. Last, First mp4 pdf 2. Last, First mp4 pdf	1. Hou, T. J.; Xu, X. J.; ADME evaluation in drug discovery. J. Mol. Model, 2002, 8, 337-349 2. Matos, DRG; Pak, S; Rizzo R; Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. J. Chem. Inf. Model. 2023	1. 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
-	-	-	Class Wrap up after QUIZ #5	-
2023.12.11 Mon Quiz #5	-	-	-	-

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