Capturing molecular energy landscapes with probabilistic conformational roadmaps

Abstract

Probabilistic roadmaps are an effective tool to compute the connectivity of the collision-free subset of high-dimensional robot configuration spaces. This paper extends them to capture the pertinent features of continuous functions over high-dimensional spaces. We focus here on computing energetically favorable motions of bio-molecules. A molecule is modeled as an articulated structure moving in an energy field. The set of all its 3D placements is the molecule's conformational space, over which the energy field is defined. A probabilistic conformational roadmap (PCR) tries to capture the connectivity of the low-energy subset of a conformational space, in the form of a network of weighted local pathways. The weight of a pathway measures the energetic difficulty for the molecule to move along it. The power of a PCR derives from its ability to compactly encode a large number of energetically favorable molecular pathways, each defined as a sequence of contiguous local pathways. This paper describes general techniques to compute and query PCRs, and presents implementations to study ligand-protein binding and protein folding.