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Robotics Algorithms for the Study of Protein Structure and Motion

Jean-Claude Latombe
Stanford University

Tuesday, April 12, 2005
CII 4050 - 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.

Proteins are the workhorses of all living organisms. They perform many vital functions, such as storage of energy, transmission of signals, transport of molecules, and defense against intruders. They are large molecules made up of hundreds to thousands of atoms. Each protein is a bonded sequence of smaller molecules (picked from a collection of twenty naturally occurring amino-acids) that determines its distinctive kinematics - a long kinematic backbone with small protruding side-chains. In robotics, this backbone would be called a highly redundant serial linkage. Key problems in structural biology include structure determination, conformation sampling, and motion simulation.

This lecture will discuss how algorithmic tools originally developed in robotics can be used to efficiently solve these problems, by exploiting the distinctive kinematics of a protein. In particular, I will consider the problem of completing partial models of protein structures resolved from electron-density maps produced by X-ray crystallography. I will describe software based on fast-inverse kinematics algorithms to recover the structure of protein fragments that yield fuzzy electron density maps. This work was done in collaboration with Dr. Henry van den Bedem and Ashley Deacon at the Joint Center for Structural Genomics (Stanford Linear Accelerator Center). Several protein structures obtained using this new software have recently been deposited in the Protein Data Bank.

I will also consider the problems of sampling protein conformations and simulating motion using a Monte Carlo approach. Here, the main computational bottleneck is to update the proximity relation among the protein atoms. I will describe a new method - the ChainTree method - that significantly speeds up Monte Carlo simulation runs without affecting their outcomes. ChainTree is based on hierarchical distance computation and collision detection techniques originally developed in robotics and computer graphics. Overall, the results presented in this lecture demonstrate that one can achieve major computational gains in computational biology by exploiting specific structural properties of molecules. These results are mostly based on the recent PhD of Itay Lotan.

Short Bio: Jean-Claude Latombe is the Kumagai Professor of Computer Science at Stanford University. He received his PhD from the National Polytechnic Institute of Grenoble (INPG) in 1977. He was on the faculty of INPG from 1980 to 1984, then he joined ITMI (Industry and Technology for Machine Intelligence), a company that he had co-founded in 1982. He moved to Stanford in 1987. At Stanford, he served as the Chairman of the Computer Science Department from 1997 till 2001, and on the BioX Leadership Council from 2002 till 2004. His main research interests are in Artificial Intelligence, Robotics, Computational Biology, Computer-Aided Surgery, and Graphic Animation.
URL: http://ai.stanford.edu/~latombe

Last updated: March 2, 2005