de-orphaning the structural proteome through reciprocal comparison of evolutionarily important structural featuresde-orphaning进化的结构蛋白质组通过相互比较重要的结构特征.pdfVIP

De-Orphaning the Structural Proteome through Reciprocal Comparison of Evolutionarily Important Structural Features 1,2 1 1 1,2 1 R. Matthew Ward , Serkan Erdin , Tuan A. Tran , David M. Kristensen , Andreas Martin Lisewski , Olivier Lichtarge1,2* 1 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America, 2 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, United States of America Abstract Function prediction frequently relies on comparing genes or gene products to search for relevant similarities. Because the number of protein structures with unknown function is mushrooming, however, we asked here whether such comparisons could be improved by focusing narrowly on the key functional features of protein structures, as defined by the Evolutionary Trace (ET). Therefore a series of algorithms was built to (a) extract local motifs (3D templates) from protein structures based on ET ranking of residue importance; (b) to assess their geometric and evolutionary similarity to other structures; and (c) to transfer enzyme annotation whenever a plurality was reached across matches. Whereas a prototype had only been 80% accurate and was not scalable, here a speedy new matching algorithm enabled large-scale searches for reciprocal matches and thus raised annotation specificity to 100% in both positive and negative controls of 49 enzymes and 50 non-enzymes, respectively—in one case even identifying an annotation error—while maintaining sensitivity (,60%). Critically, this Evolutionary Trace Annotation (ETA) pipeline requires no prior knowledge