Capturing molecular energy landscapes with probabilistic conformational roadmaps

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.