STRUCTURAL BIOINFORMATICS II Spring 2018 Syllabus Course Number - Classification: Chemistry 5412 Class Schedule: Monday 5:30-7:50 PM, SERC Room 456 (4 th floor) Instructors: Ronald Levy, SERC 718 (ronlevy@temple.edu) (course coordinator) Vincenzo Carnevale, SERC 704 (vincenzo.carnevale@temple.edu) Roland Dunbrack, FCCC, (roland.dunbrack@gmail.com) Alan Haldane, SERC 718 (alan.haldane@temple.edu) Office hours: By appointment Description This course is designed to provide a basic introduction to computational methods used in protein structure determination and molecular modeling. The course emphasis will be on the use of computational methods for protein structure prediction to understand protein structures through modelling and on structure based drug design. The course will provide practical training in the application of modeling techniques in drug discovery Objectives The objective of this course is to provide an introductory overview of concepts involved in protein structure determination using homology modelling, molecular simulations, and their role in understanding the physical basis of the structure and function of biological macromolecules. The course is aimed at providing students with a strong grounding in modeling approaches used in drug discovery. Upon successful completion, students are expected to have knowledge of various techniques used for structure determination and acquire skillsets for visualizing and analyzing macromolecular structures. In addition, students will be competent in carrying out basic molecular modelling simulations and be familiarized with structure based drug design strategies and software. Organization Some of the lectures will be held in the computer lab to keep a close connection between theoretical concepts and computational case studies. Most of the computational work will be
performed in class; however specific tasks will be completed by the students and evaluated as homework assignments. Materials In addition to the following reference books, additional study materials will be made available in class. Ø Gregory A. Petsko and Dagmar Ringe. Protein Structure and Function, (1 st Edition), New Science Press Ltd, 2004. Ø Thomas E. Creighton. Proteins: Structure and Molecular Properties, (2 nd Edition), Freeman, W. H. & Company, 1992. Ø Andrew Leach. Molecular Modelling: Principles and Applications, (2nd Edition), Prentice Hall, 2001. Ø Alan Hinchliffe. Molecular Modelling for Beginners, (2nd Edition) John Wiley & Sons, 2008. Ø Hans-Dieter Höltje, Wolfgang Sippl, Didier Rognan, Gerd Folkers. Molecular Modeling: Basic Principles and Applications,(3rd Edition) Wiley-VCH Verlag GmbH, 2008. Ø Daan Frenkel, Berend Smit. Understanding Molecular Simulation From Algorithms to Applications, (2nd Edition) Academic Press, 2001. Grading Grading is assigned on the basis of homework and lab work. They will be equally weighted. Requirements In addition to homework assignments, each student will be required to complete an individual project before the end of the course. Students are strongly encouraged to propose their own project topics.
SECOND SEMESTER 1. Protein structure modeling (7 Lectures) Lecture: 1 Homology detection for template identification: Sequence-sequence alignment- Blast Position-specific substitution matrices- PSI-Blast, 1/22/2018 Dr. Carnevale Lecture: 2 1/29/2018 Dr. Carnevale Building a profile HMM from an alignment and aligning sequences to it: Formal definition of HMMs Most probable state path: the Viterbi algorithm The forward algorithm Posterior decoding Parameter estimation for HMMs Lecture: 3 2/5/2018 Dr. Haldane Protein Sequence Databases and Practical use of HMMs Practical HMM usage: Hmmer, statistical significance, E/p-values and limitations HMMs for family alignment and for secondary structure prediction Tour of the protein sequence space: Pfam, Interpro, SCOP Lecture: 4 2/12/2018 Dr. Haldane Potts models and statistical inference of evolutionary sequence covariation Overview of protein evolution and origins of sequence variation Protein Fitness, Marginal stability, Epistasis, Compensatory Mutations, Covariation Constructing Potts Models from MSAS Potts Model Applications Lecture:5 2/19/2018 Dr. Dunbrack Template based protein modelling Homology modeling Threading or Fold recognition Loop modelling
Template Based and Non-Template Based Techniques Lecture: 6 2/26/2018 Dr. Dunbrack Protein side chain modelling Graph based, Tree Decomposition, DEE, SCMF Refinement of comparative models Model quality assessment Errors in protein modelling Lecture: 7 (Computer lab with lecture) 3/12/2018 Dr. Dunbrack Hands on session in Protein Modelling (Rosetta) 2. Molecular Modeling (3 Lectures) Lecture 8 3/19/2018 Dr. Levy Molecular Mechanics Force fields: general features of molecular mechanics, bonded terms, non-bonded terms, and related parameterization strategy. Solvation effects: explicit and implicit models Lecture 9 3/26/2018 Dr. Levy Conformational Sampling Methods Basics of Monte Carlo simulations: Metroplis algorithm. Basics of molecular dynamics simulations: Verlet algorithm. Free Energy Simulations and Protein-Ligand Binding Lecture 10: MD Lab 4/2/2018 Dr. Levy Hands on session of MD simulations of HIV Integrase-Ligand interactions. (Desmond package) Building the simulated complex, energy minimization, adding solvation models, molecular dynamics (MD) simulations, post-simulation analysis (Energy and structure fluctuations) 3. Structure Based Drug Design (4 lectures) Lecture 11 4/09/2018 Dr. Carnevale Virtual screening in Drug Discovery Overview of ligand-based and structure-based screening, basics of Molecular Docking. Success stories form structure based drug design: HIV-1 protease inhibitor.
Lecture 12 4/16/2018 Dr. Karanicolas Computational chemical biology to address non-traditional drug targets Lecture 13 4/23/2018 Dr. Gianti Best practices in Virtual Screening: ligand preparation, protein preparation, benchmarking using various evaluation metrics (ROC enrichment, RMSD for pose prediction) Lecture 14: Docking Lab 4/30/2018 Dr. Carnevale Virtual Screening: Docking using Glide (Schrodinger)