title: Building and assessing atomic models of proteins from structural templates: learning and benchmarks.
abstract: 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.
authors.affiliation: Department of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
authors.forename: Brinda Kizhakke
authors.initials: BK
authors.lastname: Vallat
authors.forename: Jaroslaw
authors.initials: J
authors.lastname: Pillardy
authors.forename: Peter
authors.initials: P
authors.lastname: Májek
authors.forename: Jaroslaw
authors.initials: J
authors.lastname: Meller
authors.forename: Thomas
authors.initials: T
authors.lastname: Blom
authors.forename: Baoqiang
authors.initials: B
authors.lastname: Cao
authors.forename: Ron
authors.initials: R
authors.lastname: Elber