<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chen, B. Y.</style></author><author><style face="normal" font="default" size="100%">Bryant, Drew H</style></author><author><style face="normal" font="default" size="100%">Cruess, A. E.</style></author><author><style face="normal" font="default" size="100%">J. H. Bylund</style></author><author><style face="normal" font="default" size="100%">Viacheslav Y. Fofanov</style></author><author><style face="normal" font="default" size="100%">Marek Kimmel</style></author><author><style face="normal" font="default" size="100%">Olivier Lichtarge</style></author><author><style face="normal" font="default" size="100%">L. E. Kavraki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Composite Motifs Integrating Multiple Protein Structures Increase Sensitivity for Function Prediction</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Systems Bioinformatics Conference (CSB2007)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword><keyword><style  face="normal" font="default" size="100%">protein function</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">343-355</style></pages><abstract><style face="normal" font="default" size="100%">The study of disease often hinges on the biological function of proteins, but determining protein function is a diﬃcult experimental process. To minimize duplicated eﬀort, algorithms for function prediction seek characteristics indicative of possible protein function. One approach is to identify substructural matches of geometric and chemical similarity between motifs representing known active sites and target protein structures with unknown function. In earlier work, statistically signiﬁcant matches of certain eﬀective motifs have identiﬁed functionally related active sites. Eﬀective motifs must be carefully designed to maintain similarity to functionally related sites (sensitivity) and avoid incidental similarities to functionally unrelated protein geometry (speciﬁcity). Existing techniques design motifs using the geometry of a single protein structure. Poor selection of this structure can limit motif eﬀectiveness if the selected functional site lacks similarity to functionally related sites. To address this problem, this paper presents composite motifs, which combine structures of functionally related active sites to potentially increase sensitivity. Our experimentation compares the eﬀectiveness of composite motifs with simple motifs designed from single protein structures. On six distinct families of functionally related proteins, leave-one-out testing showed that composite motifs had sensitivity comparable to the most sensitive of all simple motifs and speciﬁcity comparable to the average simple motif. On our data set, we observed that composite motifs simultaneously capture variations in active site conformation, diminish the problem of selecting motif structures, and enable the fusion of protein structures from diverse data sources. 
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