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), has proven effective on problems from all these domains.

In this talk, we describe our 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 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.

This is joint work with Guang Song, a PhD student at Texas A&M, and Ken Dill at UCSF. More information regarding our work, including movies, can be found at http://www.cs.tamu.edu/faculty/amato/.

Note time and location change
Tuesday, January 29, 2002 at 3:30pm in DH 1070
A reception will follow in DH 3092

About Nancy Amato

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 of the IEEE Transactions on Parallel and Distributed Computing, 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), the State of Texas, Boeing, and GE. She is a member of the Computing Research Association's Committee on the Status of Women in Computing Research (CRA-W) and she coordinates the CRA-W Distributed Mentoring Project.