Fast and accurate travel depth estimation for protein active site prediction

Active site prediction, well-known for drug design and medical diagnosis, is a major step in the study and prediction of interactions between proteins. The specialized literature provides studies of common physicochemical and geometric properties shared by active sites. Among these properties, this paper focuses on the travel depth which takes a major part in the binding with other molecules. The travel depth of a point on the protein solvent excluded surface (SES) can be defined as the shortest path accessible for a solvent molecule between this point and the protein convex hull. Existing algorithms providing an estimation of this depth are based on the sampling of a bounding box volume surrounding the studied protein. These techniques make use of huge amounts of memory and processing time and result in estimations with precisions that strongly depend on the chosen sampling rate. The contribution of this paper is a surface-based algorithm that only takes samples of the protein SES into account instead of the whole volume. We show this technique allows a more accurate prediction, at least 50 times faster. A validation of this method is also proposed through experiments with a statistical classifier taking as inputs the travel depth and other physicochemical and geometric measures for active site prediction.