Building and assessing atomic models of proteins from structural templates: learning and benchmarks.

One approach to predict a protein fold from a sequence (a target) is based on structures of related proteins that are used as templates. We present an algorithm that examines a set of candidates for templates, builds from each of the templates an atomically detailed model, and ranks the models. The algorithm performs a hierarchical selection of the best model using a diverse set of signals. After a quick and suboptimal screening of template candidates from the protein data bank, the current method fine-tunes the selection to a few models. More detailed signals test the compatibility of the sequence and the proposed structures, and are merged to give a global fitness measure using linear programming. This algorithm is a component of the prediction server LOOPP (http://www.loopp.org). Large-scale training and tests sets were designed and are presented. Recent results of the LOOPP server in CASP8 are discussed. Copyright 2009 Wiley-Liss, Inc.