Special Topics in Computational Biology: Rational Design of Proteins and Drugs 15-873

Instructor

Chris Langmead, WeH 4103, cjl at cs.cmu.edu

Description
We will read a series of papers on algorithms for drug design and protein (re)design. We will discuss these algorithms from a variety of perspectives: their complexity, their fidelity to the underlying physics, and their overall accuracy when applied. Students will be required to read present papers and prepare short written summaries of each paper they present.

Prerequisites:

15-879 or permission of instructor

Course Information

Classes: T, TH 6:30 - 7:50 PM ; WeH 4615A

Office Hours: by appointment

Note: This class does not begin until September 13, due to the SCS immigration process

Syllabus

Date	Topic	Reading	Speaker
Pre-class reading
13-Sep	Introduction to course, overview of protein and drug design.	(1) Protein Design Concepts, Steip; (2) [handout] Drug design and discovery: an overview, Mitscher (3) Protein Docking, Kaapro and Ojanen; (4) [handout] Computer-aided development and use of three-dimensional pharmacophore models, Liljefors and Pettersson	Chris Langmead
15-Sep	Hardness Results	(1) Protein design is NP-Hard, Pierce and Winfree; (2) The Inapproximability of Side-Chain Positioning, Chazelle et al;	Group discussion
20-Sep	Force Fields and Energy Calculations	(1) Factors Affecting the Ability of Energy Functions to Discriminate Correct from Incorrect Folds, Park et al; (2) On the design and analysis of protein folding potentials, Tobi et al	Group discussion
22-Sep	Force Fields and Energy Calculations; Drug Design: Rigid Docking 1	(1) BIOMOLECULAR SIMULATIONS: Recent Developments in Force Fields, Simulations of Enzyme Catalysis, Protein-Ligand, Protein-Protein, and Protein-Nucleic Acid Noncovalent Interactions, Wang (2) Modelling Protein Docking using Shape Complementarity, Electrostatics and Biochemical Information, Gabb et al;	Group discussion
27-Sep	Drug Design: Rigid Docking 2	(1) Efficient Unbound Docking of Rigid Molecules, Duhovny et al; (2) Critical Evaluation of Search Algorithms for Automated Molecular Docking and Database Screening, Ewing and Kuntz;	Group discussion
29-Sep	Drug Design: Rigid Docking 3	(1) Docking Unbound Proteins with MIAX: A Novel Algorithm for Protein-Protein Soft Docking, Munoz et al; (2) Rigid Body Docking for Virtual Screening, May et al;	Group discussion
4-Oct	Drug Design: Flexible Docking 1	(1) Development and Validation of a Genetic Algorithm for Flexible Docking, Jones et al; (2) Improved protein-ligand docking using GOLD, Verdonk et al	Hetu Kamisetty
6-Oct	Drug Design: Flexible Docking 2	(1) A Fast Flexible Docking Method using an Incremental Construction Algorithm, Rarey et al; (2) A method for Biomolecular Structural Recognition and Docking Allowing Conformational Flexibility, Sandak et al;	Group discussion
11-Oct	Drug Design: Ensemble Docking	(1) FLEXE: Efficient Molecular Docking Considering Protein Structure Variations, Clauben et al; (2) Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm Based on Molecular Dynamics, Taufer et al;	Bob Montgomery
13-Oct	Drug Design: Ensemble Docking, Scoring Functions 1	(1) Molecular Docking to Ensembles of Protein Structures, Knegtel et al; (2) SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex, Wang et al;	Jacob Joseph
18-Oct	Drug Design: Scoring Functions 2	(1) Knowledge-based Scoring Function to Predicting Protein-Ligand Interactions, Gohlke et al; (2) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations, Bissantz et al;	Madhavi Ganapathiraju
20-Oct	Drug Design: Pharmacophores	(1) RAPID: Randomized Pharmacophore Identi cation for Drug Design, Finn et al; (2) Approximation algorithms for 3-D common substructure identification in drug and protein molecules, Chakraborty and Biswas	Sumit Jha
25-Oct	Drug Design: Machine Learning	(1) Active Learning in the Drug Discovery Process, Warmuth et al; ______ ___ _____ ___ _ _________ _______________ ___!_!#"__%$ &'_ __$_"__(_ _________ _*)+"__(,-______. _/___ __$0____/12___ $__ 34__/15___%_ (2) Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis, Burbidge et al;	Amina Abdulla
27-Oct	Drug Design: Machine Learning		Amina Abdulla
1-Nov	Protein Design/Side Chain Placement: Dead End Elimination 1	(1) [handout] The dead-end elimination theorem and its use in protein Side-chain positioning, Desmet et al; (2) Theoretical and Algorithmical Optimization of the Dead-End Elimination Theorem, Desmet and Lasters;	Naveena Yanamala
3-Nov	Protein Design/Side Chain Placement: Dead End Elimination 2	(1) Conformational Splitting: A more powerful criterion for dead-end elimination, Pierce et al (2) Generalized Dead-end Elimination Algorithms Make Large-scale Protein Side-chain Structure Prediction Tractable: Implications for Protein Design and Structural Genomics, Looger and Hellinga;	Jacob Joseph
8-Nov	Protein Design/Side Chain Placement: Mean Field Theory and Graph-Theoretic approaches	(1) Application of a Self-Consistent Mean Field Theory to Predict Protein Side-Chains Conformations and Estimate Their Conformational Entropy, Koehl and Delarue; (2) A graph-theory algorithm for rapid protein side-chain prediction, Canutescu et al;	Sumit Jha
10-Nov	Protein Design/Side Chain Placement: Branch and Bound and A*	(1) A Multi-Queue Branch-and-Bound Algorithm for Anytime Optimal Search with Biological Applications, Lathrop et al; (2) Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm, Leach and Lemon	Bob Montgomery
15-Nov	Protein Design/Side Chain Placement: Genetic Algorithms	(1) De novo protein design using pairwise potentials and a genetic algorithm, Jones (2) GEM: A Gaussian evolutionary method for predicting protein side-chain conformations, Yang et al	Naveena Yanamala
17-Nov	Protein Design: Monte Carlo 1	(1) The Evolutionary Capacity of Protein Structures, Meyerguz et al (2) Efficient Algorithms for Protein Sequence Design and the Analysis of Certain Evolutionary Fitness Landscapes, Kleinberg	Hetu Kamisetty
22-Nov	Protein Design: Monte Carlo 2; comparison of methods	(1) Recapitulation of Protein Family Divergence using Flexible Backbone Protein Design, Saunders and Baker (2) Trading Accuracy for Speed: A Quantitative Comparison of Search Algorithms in Protein Sequence Design, Voigt et al;	Bob Montgomery
24-Nov	NO CLASS
29-Nov	Drug Design: Machine Learning	(1) Kernel Functions for Attributed Molecular Graphs – A New Similarity Based Approach To ADME Prediction in Classification and Regression, Frohlick et al;	Amina Abdulla
1-Dec	Protein Design:	(1) Automatic procedures for protein design, Jaramillo et al; (2) Probing sequence-structure relationships in proteins: Application of simple energy functions to the inverse folding problem, Elber et al;	Madhavi Ganapathiraju
6-Dec	Protein Design: Design for function and protein complexes	(1) Rational design of faster associating and tighter binding protein complexes, Selzer et al (2) A Novel EnsembleBased Scoring and Search Algorithm for Protein Redesign, and its Application to Modify the Substrate Specificity of the Gramicidin Synthetase A Phenylalanine Adenylation Enzyme; Lilien et al;	Aly Azeem Khan
8-Dec	Protein Design: protein-DNA interactions, nucleic acid design	(1) A Simple Physical Model for the Prediction and Design of Protein–DNA Interactions; Havranek et al (2) Paradigms for computational nucleic acid design, Dirks et al	Aly Azeem Khan

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Useful Links

IUPAC Glossary of Terms Used in Computational Drug Design

An Introduction to QSAR methodology

Drug Discovery Tutorial

Some Papers we won’t get to, but might be useful as background

Molecular Modeling, Broughton;

Computer simulations of ligand-protein binding with ensembles of protein conformations: A Monte Carlo study of HIV-1 protease binding energy landscapes, Bouzida et al

De Novo Protein Design I: In Search of Stability and Specificity, Koehl and Levitt

De Novo Protein Design II: In Plasticity In Sequence Space, Koehl and Levitt

Protein design: a perspective from simple tractable models, Shakhnovich;