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Most of the previous multitemplate approaches have built a \u201csuper\u201d template with very little merging of aligned fragments from different templates. Here, we present a parallelized multitemplate approach, patGPCR, to predict the 3D structures of transmembrane helices of G-protein-coupled receptors. patGPCR, which employs a bundle-packing related energy function that extends on the RosettaMem energy, parallelizes eight pipelines for transmembrane helix refinement and exchanges the optimized helix structures from multiple templates. We have investigated the performance of patGPCR on a test set containing eight determined G-protein-coupled receptors. The results indicate that patGPCR improves the TM RMSD of the predicted models by 33.64% on average against a single-template method. Compared with other homology approaches, the best models for five of the eight targets built by patGPCR had a lower TM RMSD than that obtained from SWISS-MODEL; patGPCR also showed lower average TM RMSD than single-template and multiple-template MODELLER.<\/jats:p>","DOI":"10.1155\/2013\/486125","type":"journal-article","created":{"date-parts":[[2013,3,11]],"date-time":"2013-03-11T17:00:48Z","timestamp":1363021248000},"page":"1-12","source":"Crossref","is-referenced-by-count":2,"title":["patGPCR: A Multitemplate Approach for Improving 3D Structure Prediction of Transmembrane Helices of G-Protein-Coupled Receptors"],"prefix":"10.1155","volume":"2013","author":[{"given":"Hongjie","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"},{"name":"Jiangsu Provincial Key Lab for Information Processing Technologies, Suzhou 215006, China"},{"name":"School of Electronic and Information Engineering, Suzhou 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