Abstract. The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Resolving these issues depends partially on a broad and thorough understanding of the biological function of many proteins, but the experimental determination of protein function is expensive and time consuming. In response, 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. One approach is to identify matches of geometric and chemical similarity between motifs, representing known functional sites, and substructures of functionally uncharacterized proteins (targets). Matches of statistically significant geometric and chemical similarity can identify targets with active sites cognate to the matching motif. This paper first summarizes the MASH (Match Augmentation with Statistical Hypothesis Testing) pipeline for protein function prediction. MASH combines the Match Augmentation algorithm for efficiently identifying matches with a statistical model for assessing the significance of matches found. MASH has been shown capable of identifying statistically significant matches in functional homologs. However, MASH also makes some incorrect predictions when it identifies statistically significant matches in functionally unrelated proteins. Reducing incorrect predictions is critical, because incorrect predictions can lead to costly mistakes when experimentation is used to verify computational predictions. An effective function predictor requires effective motifs -motifs whose geometric and chemical characteristics are detected by comparison algorithms within functionally homologous targets (sensitive motifs), which also are not detected within functionally unrelated targets (specific motifs). Designing effective motifs is a difficult open problem. Current approaches select and combine structural, physical, and evolutionary properties to design motifs that mirror functional characteristics of active sites. We present a new approach, Geometric Sieving (GS), which refines candidate motifs into optimized motifs with maximal geometric and chemical dissimilarity from all known protein structures. We refer to this property as Geometric Uniqueness. The paper discusses both the usefulness and the efficiency of GS. We show that candidate motifs from 10 well studied proteins, including α-Chymotrypsin, Dihydrofolate Reductase, and Lysozyme, can be optimized with GS to motifs that are among the most sensitive and specific motifs possible for the candidate motifs. For the same proteins, we also report results that relate evolutionarily important motifs with motifs that exhibit maximal geometric and chemical dissimilarity from all known protein structures. Our current observations show that GS is a powerful tool that can complement existing work on motif design and protein function prediction.
REF the MASH matching algorithm is introduced.
The MASH Pipeline for Protein Function Prediction and an Algorithm for the Geometric Refinement of 3D Motifs
Journal of computational biology : a journal of computational molecular cell biology
Journal of computational biology : a journal of computational molecular cell biology
Mathematics Medicine Computer Science