AMS 535, Introduction to Computational Structural Biology and Drug Design
This course will provide an introduction to Computational Structural Biology with
application to Drug Design. Methods and applications that use computation to model
biological systems involved in human disease will be emphasized. The course aims to
foster collaborative learning and will consist of presentations by the instructor,
guest lecturers, and by course participants with the goal of summarizing key methods,
topics, and papers relevant to Computational Structural Biology. This course is offered
as both AMS 535 and CHE 535.
3 credits, ABCF grading
Text: No textbook
Fall Semester
Learning Outcomes:
1) Become informed about the field of computational structure-based drug design and the pros and cons.
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:
* Drug Discovery and Biomolecular Structure;
* Molecular Modeling;
* Sampling Methods;
* Lead Discovery;
* Lead Refinement.
5) Drug Discovery, Chemistry Review, Proteins, Carbohydrates, Nucleic acids.
6) Molecular Interactions and Recognition, Experimental Techniques for Elucidating Structure.
7) Classical Force Fields (Molecular Mechanics).
8) Solvent Models, Condensed-phase Calculations, Parameter Development.
9) Conformational Space, Molecular Dynamics (MD), Metropolis Monte Carlo (MC).
10) Sampling Techniques, Predicting Protein Structure, Protein Folding.
11) Docking as a Lead Generation Tool, Docking Algorithms.
12) Discovery Methods I, Discovery Methods II, Applications.
13) Free Energy Perturbation (FEP), Linear Response (LR), Extended Linear Response (ELR).
14) MM-PBSA, MM-GBSA, Properties of Known Drugs, Property Prediction.