CSRI Seminar: Nancy Amato

Title	Using Motion Planning to Study Protein Folding
Speaker E-mail From	Nancy Amato amato@cs.tamu.edu Texas A&M University
Date Time Location	Tuesday, November 6, 2001 10-11am (PST) Bldg. 940, Auditorium (IMTL) (Sandia - CA) Bldg. 980, Room 24 (Sandia - NM)
Abstract	Motion planning arises not only in robotics but in many other areas such as intelligent CAD (virtual prototyping), mixed reality systems (training and computer-assisted operation), and even computational biology and chemistry (protein folding and drug design). Surprisingly, a single class of planners, called probabilistic roadmap methods (PRMs), have proven effective on problems from all these domains. Strengths of PRMs, in addition to versatility, are simplicity and efficiency even in high-dimensional configuration spaces. Moreover, PRMs are (almost) embarrassingly parallel. In the first part of this talk, we introduce the PRM framework, briefly describe several PRM variants developed in our group, and discuss parallelizing PRMs. In the second part, we concentrate on our recent application of PRM-based motion planning techniques to protein folding. Our focus in this work is to study the protein folding mechanism assuming we know the native fold. Therefore, instead of performing fold prediction, we aim to study issues related to the folding process, such as the formation of secondary and tertiary structure, and the dependence on the initial conformation. Our results on several small to moderate sized proteins (60-150 amino acids) indicate that the PRM-based technique generates folding pathways that are in agreement with experimental data. Our technique naturally supports the study of folding pathways starting from any desired denatured starting conformation, and also appears to differentiate between proteins where secondary structure forms first and those where the tertiary structure is obtained more directly. If time allows, we will describe initial promising results using PRMs for ligand/protein binding; this work utilizes haptic user input. More information regarding our work, including movies, can be found at http://www.cs.tamu.edu/faculty/amato
About the Speaker	Nancy M. Amato joined Texas A&M University in January 1995, where she is now an associate professor. She received B.S. and A.B. degrees in Mathematical Sciences and Economics, respectively, from Stanford University, and M.S. and Ph.D. degrees in Computer Science from UC Berkeley and the University of Illinois at Urbana-Champaign. Her main areas of research focus are motion planning (with application to computational biology and virtual prototyping), high-performance computing, and computational geometry. She is an Associate Editor of the IEEE Transactions on Robotics and Automation, and she regularly serves on the program committees of conferences and NSF review panels in the areas of robotics, high-performance computing, and computational geometry. Her research is supported by the National Science Foundation, the Department of Energy (ASCI program), Boeing, and GE.

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