%A Brylinski,Michal %D 2013 %J Frontiers in Genetics %C %F %G English %K protein function inference,template-based modeling,protein threading,meta-threading,eThread,ligand binding,metal binding,iron-sulfur binding,protein-protein interactions,protein-DNA interactions %Q %R 10.3389/fgene.2013.00118 %W %L %M %P %7 %8 2013-June-19 %9 Methods %+ Dr Michal Brylinski,Louisiana State University,Department of Biological Sciences,Louisiana State University,202 Life Sciences Bldg,Baton Rouge,70803,LA,United States,michal@brylinski.org %+ Dr Michal Brylinski,Louisiana State University,Center for Computation & Technology,214 Johnston Hall,Baton Rouge,70803,LA,United States,michal@brylinski.org %# %! Evolution/structure-based function inference of proteins %* %< %T Unleashing the power of meta-threading for evolution/structure-based function inference of proteins %U https://www.frontiersin.org/articles/10.3389/fgene.2013.00118 %V 4 %0 JOURNAL ARTICLE %@ 1664-8021 %X Protein threading is widely used in the prediction of protein structure and the subsequent functional annotation. Most threading approaches employ similar criteria for the template identification for use in both protein structure and function modeling. Using structure similarity alone might result in a high false positive rate in protein function inference, which suggests that selecting functional templates should be subject to a different set of constraints. In this study, we extend the functionality of eThread, a recently developed approach to meta-threading, focusing on the optimal selection of functional templates. We optimized the selection of template proteins to cover a broad spectrum of protein molecular function: ligand, metal, inorganic cluster, protein, and nucleic acid binding. In large-scale benchmarks, we demonstrate that the recognition rates in identifying templates that bind molecular partners in similar locations are very high, typically 70–80%, at the expense of a relatively low false positive rate. eThread also provides useful insights into the chemical properties of binding molecules and the structural features of binding. For instance, the sensitivity in recognizing similar protein-binding interfaces is 58% at only 18% false positive rate. Furthermore, in comparative analysis, we demonstrate that meta-threading supported by machine learning outperforms single-threading approaches in functional template selection. We show that meta-threading effectively detects many facets of protein molecular function, even in a low-sequence identity regime. The enhanced version of eThread is freely available as a webserver and stand-alone software at http://www.brylinski.org/ethread.