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.
Vallat
Brinda Kizhakke
BK
Department of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
Pillardy
Jaroslaw
J
Májek
Peter
P
Meller
Jaroslaw
J
Blom
Thomas
T
Cao
Baoqiang
B
Elber
Ron
R