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Geometric Methods in Structural Computational Biology

Collection type: Course

Course by: Lydia E. Kavraki

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Summary: This course is a short series of lectures on Structural Computational Biology, with an emphasis on geometric algorithms. Topics covered include basic data structures for modeling proteins; kinematics and inverse kinematics of protein chains; distance measures and alignment algorithms for protein structures; motif finding for the functional annotation of proteins; the application of robotics-derived methods to problems in protein modeling; and protein-ligand docking. The development of this course has been supported by NSF 0203396. A number of people have contributed to the development of this course and include (in alphabetical order) Kostas Bekris, Brian Chen, Allison Heath, Mark Moll, Miguel Teodoro, Amarda Shehu, David Schwarz, and Hernan Stamati. The authors are grateful to Jean-Claude Latombe for his help in the initial stages of this project and to the many students who have taken this course at Rice University and have provided useful feedback.

Instructor: Lydia E. Kavraki

Institution: Rice University

Course Number: COMP 470

This collection contains: Modules by: Lydia E. Kavraki

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