Literature DB >> 25014909

GPCRserver: an accurate and novel G protein-coupled receptor predictor.

Renxiang Yan1, Xiaofeng Wang, Lanqing Huang, Jun Lin, Weiwen Cai, Ziding Zhang.   

Abstract

G protein coupled receptors (GPCRs), also known as seven-transmembrane domain receptors, pass through the cellular membrane seven times and play diverse biological roles in the cells such as signaling, transporting of molecules and cell-cell communication. In this work, we develop a web server, namely the GPCRserver, which is capable of identifying GPCRs from genomic sequences, and locating their transmembrane regions. The GPCRserver contains three modules: (1) the Trans-GPCR for the transmembrane region prediction by using sequence evolutionary profiles with the assistance of neural network training, (2) the SSEA-GPCR for identifying GPCRs from genomic data by using secondary structure element alignment, and (3) the PPA-GPCR for identifying GPCRs by using profile-to-profile alignment. Our predictor was strictly benchmarked and showed its favorable performance in the real application. The web server and stand-alone programs are publicly available at .

Mesh:

Substances:

Year:  2014        PMID: 25014909     DOI: 10.1039/c4mb00272e

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  2 in total

1.  Prediction of structural features and application to outer membrane protein identification.

Authors:  Renxiang Yan; Xiaofeng Wang; Lanqing Huang; Feidi Yan; Xiaoyu Xue; Weiwen Cai
Journal:  Sci Rep       Date:  2015-06-24       Impact factor: 4.379

2.  DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.

Authors:  Xiaofeng Wang; Renxiang Yan; Jiangning Song
Journal:  Sci Rep       Date:  2016-03-22       Impact factor: 4.379

  2 in total

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