patGPCR: A Multitemplate Approach for Improving 3D Structure Prediction of Transmembrane Helices of G-Protein-Coupled ReceptorsReport as inadecuate

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Computational and Mathematical Methods in MedicineVolume 2013 2013, Article ID 486125, 12 pages

Research Article

School of Computer Science and Technology, Soochow University, Suzhou 215006, China

Jiangsu Provincial Key Lab for Information Processing Technologies, Suzhou 215006, China

School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Received 5 November 2012; Revised 10 January 2013; Accepted 16 January 2013

Academic Editor: Hong-Bin Shen

Copyright © 2013 Hongjie Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The structures of the seven transmembrane helices of G-protein-coupled receptors are critically involved in many aspects of these receptors, such as receptor stability, ligand docking, and molecular function. Most of the previous multitemplate approaches have built a -super- 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.

Author: Hongjie Wu, Qiang Lü, Lijun Quan, Peide Qian, and Xiaoyan Xia



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