Apaydin, M. S., A. P. Singh, D. L. Brutlag and J. C. Latombe (2001). "Capturing Molecular Energy Landscapes with Probabilistic Conformational Roadmaps." International Conference on Robotics and Automatons 2001: 932-939..
Probabilistic roadmaps have proven to be an effective tool to compute the connectivity of the collision-free subset of high-dimensional robot configuration. This paper extends them to capture the pertinent features of continuous functions over high-dimensional spaces. Although this extension has several possible applications (e.g. the planning of minimally-invasive surgical procedures), this paper focuses on the computation of energetically favorable motions of bio-molecules. Many bio-chemical processes essential to life depend on the ability of 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 difficulty for the molecule to move along it. The power of a PCR derives from its ability to compactly encode a huge number of energetically favorable molecular pathways, each defined as a sequence of contiguous local pathways. This paper first describes general techniques to compute and query PCRs. It then presents implementations of these techniques to study ligand-protein binding and (still at a very preliminary stage) protein folding, and gives experimental results.
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