Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 76, Issue 4, Pages 930-945Publisher
WILEY
DOI: 10.1002/prot.22401
Keywords
homology modeling; mathematical programming; feature selection; structure determination
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Funding
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM059796, R01GM067823] Funding Source: NIH RePORTER
- NIGMS NIH HHS [R01 GM067823-07, R01 GM067823-06, R01 GM067823, R01 GM059796, GM067823] Funding Source: Medline
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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.
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