Abstract
Prediction of promiscuous coupling between G-protein coupling receptors (GPCRs) and various G-proteins is one of the challenges for current research. In this article, a novel method that based on the optimization of the coupling regions is proposed. The cross validation results demonstrate that it can achieve an average accuracy of over 80% for promiscuous coupling prediction. The results further suggest that the optimized coupling regions may be important for the processes of signal transduction pathways.
Keywords: G-protein coupled receptor, learning method, promiscuous coupling, signal transduction pathways, databases of Gproteins, intracellular regions, extracellular regions, Support Vector Machine, Performance Evaluation, Hidden Markov model
Combinatorial Chemistry & High Throughput Screening
Title:Predicting Coupling Specificity of GPCRs Based on the Optimization of the Coupling Regions
Volume: 15 Issue: 9
Author(s): Ran Tao, Zhenran Jiang, Weiming Yu and Junxiang Wang
Affiliation:
Keywords: G-protein coupled receptor, learning method, promiscuous coupling, signal transduction pathways, databases of Gproteins, intracellular regions, extracellular regions, Support Vector Machine, Performance Evaluation, Hidden Markov model
Abstract: Prediction of promiscuous coupling between G-protein coupling receptors (GPCRs) and various G-proteins is one of the challenges for current research. In this article, a novel method that based on the optimization of the coupling regions is proposed. The cross validation results demonstrate that it can achieve an average accuracy of over 80% for promiscuous coupling prediction. The results further suggest that the optimized coupling regions may be important for the processes of signal transduction pathways.
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Cite this article as:
Tao Ran, Jiang Zhenran, Yu Weiming and Wang Junxiang, Predicting Coupling Specificity of GPCRs Based on the Optimization of the Coupling Regions, Combinatorial Chemistry & High Throughput Screening 2012; 15 (9) . https://dx.doi.org/10.2174/138620712803519662
DOI https://dx.doi.org/10.2174/138620712803519662 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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