Geometry - based Methods for Protein Function Prediction by Brian

The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Unfortunately, resolving these issues depends partially on a broad and thorough understanding of the biological function of many proteins, and the experimental determination of protein function is expensive and time consuming. In response to this problem, algorithms for computational function prediction have been designed to expand experimental impact by finding proteins with predictably similar function, mapping experimental knowledge onto very similar, unstudied proteins. This thesis seeks to develop one method that can identify useful geometric and chemical similarities between well studied and unstudied proteins. Our approach is to identify matches of geometric and chemical similarity between motifs, representing known functional sites, and substructures of functionally uncharacterized proteins (targets). It is commonly hypothesized that the existence of a match could imply that the target contains an active site similar to the motif. We have designed the MASH (Match Augmentation with Statistical Hypothesis Testing) pipeline, a software tool for computing matches. MASH is the first method to match point-based motifs, developed in earlier work, that represent functional sites as points in space with ranked priorities and alternative chemical labels. MASH is also first to match cavity-aware motifs, a novel contribution of this work, that extend point-based motifs with volumet-