Geometric Sieving: Automated Distributed Optimization of 3D Motifs for Protein Function Prediction

Abstract. Determining the function of all proteins is a recurring theme in modern biology and medicine, but the sheer number of proteins makes experimental approaches impractical. For this reason, current efforts have considered in silico function prediction in order to guide and accelerate the function determination process. One approach to predicting protein function is to search functionally uncharacterized protein structures (targets), for substructures with geometric and chemical similarity (matches), to known active sites (motifs). Finding a match can imply that the target has an active site similar to the motif, suggesting functional homology. 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. Curren