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Predicting G‐protein coupled receptors–G‐protein coupling specificity based on autocross‐covariance transform
Author(s) -
Guo Yanzhi,
Li Menglong,
Lu Minchun,
Wen Zhining,
Huang Zhongtian
Publication year - 2006
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21097
Subject(s) - jackknife resampling , computer science , support vector machine , pseudo amino acid composition , g protein coupled receptor , transmembrane protein , computational biology , artificial intelligence , topology (electrical circuits) , machine learning , pattern recognition (psychology) , data mining , mathematics , amino acid , biology , receptor , dipeptide , biochemistry , statistics , combinatorics , estimator
Determining G-protein coupled receptors (GPCRs) coupling specificity is very important for further understanding the functions of receptors. A successful method in this area will benefit both basic research and drug discovery practice. Previously published methods rely on the transmembrane topology prediction at training step, even at prediction step. However, the transmembrane topology predicted by even the best algorithm is not of high accuracy. In this study, we developed a new method, autocross-covariance (ACC) transform based support vector machine (SVM), to predict coupling specificity between GPCRs and G-proteins. The primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and the data are transformed into a uniform matrix by applying ACC transform. SVMs for nonpromiscuous coupled GPCRs and promiscuous coupled GPCRs were trained and validated by jackknife test and the results thus obtained are very promising. All classifiers were also evaluated by the test datasets with good performance. Besides the high prediction accuracy, the most important feature of this method is that it does not require any transmembrane topology prediction at either training or prediction step but only the primary sequences of proteins. The results indicate that this relatively simple method is applicable. Academic users can freely download the prediction program at http://www.scucic.net/group/database/Service.asp.