Applications of Motion Planning to Computational Biology
supported by NSF
Nancy Amato, O. Burchan Bayazit and Guang Song

Motion planning, as its name suggests, plans a path (motion) for a movable object. Even though it originated in, and has mainly been applied to, robotics problems, motion planning as a concept is abstract enough to be applied to any motion related application, ranging from robotics to animation, and most recently to computational biology and chemistry.

In traditional applications, the goal of the motion planner is to find collision free paths taking the movable object (the robot) between given start and goal configurations. Sometimes, it is also desired for the path to have other characteristics, such as minimum length. In computational biology and chemistry applications, the goal is to find energetically feasible paths for the protein or ligand which end in stable configurations in the potential energy landscape.

Our group is investing applications of probabilistic roadmap (PRM) motion planning methods to protein folding and ligand binding (aka drug docking, which arises in drug design). In these methods, the main primitive operation is a validity check used to determine if a given configuration is acceptable or not. In traditional applications, this validity check is typically a test for collision between the the moveable object (the robot) and other objects (the obstacles) in the workspace. In computational biology and chemistry applications, this collision check is replaced by a potential energy computation and configurations are accepted as valid if their potential is low enough.

More details on Protein Folding

More details on Ligand Binding

Using Motion Planning to Study Ligand Binding and Protein Folding, Nancy M. Amato, O. Burchan Bayazit, and Guang Song, Joint AI & Robotics Seminar and TAMBUG Meeting (Texas A&M Bioinformatics User Group), Texas A&M University, October 20, 2000.
TAMBUG Presentation ( html, ppt , compressed ppt )