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 space. This paper extends them to capture the pertinent features of continuous functions over high-dimensional spaces. This extension has several possible applications, but the focus here is on computing energetically favorable motions of bio-molecules. Many bio-chemical processes essential to life require certain molecules to adopt different shapes over time. Computational tools predicting such motions can help better understand these processes and design useful molecules (e.g., new drugs). In this context, a molecule is modeled as an articulated structure moving in an energy field. The set of all its 3-D 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.