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. 2010 Jun 16:11:325.
doi: 10.1186/1471-2105-11-325.

Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm

Zhanchao Li  1 , Xuan Zhou, Zong Dai, Xiaoyong Zou
Affiliations

Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm

Zhanchao Li et al. BMC Bioinformatics. .

Abstract

Background: Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs.

Results: In this study, a novel three-layer predictor based on support vector machine (SVM) and feature selection is developed for predicting and classifying GPCRs directly from amino acid sequence data. The maximum relevance minimum redundancy (mRMR) is applied to pre-evaluate features with discriminative information while genetic algorithm (GA) is utilized to find the optimized feature subsets. SVM is used for the construction of classification models. The overall accuracy with three-layer predictor at levels of superfamily, family and subfamily are obtained by cross-validation test on two non-redundant dataset. The results are about 0.5% to 16% higher than those of GPCR-CA and GPCRPred.

Conclusion: The results with high success rates indicate that the proposed predictor is a useful automated tool in predicting GPCRs. GPCR-SVMFS, a corresponding executable program for GPCRs prediction and classification, can be acquired freely on request from the authors.

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Figures

Figure 1
Figure 1
Random sequence consisting of 40 residues as an example to illustrate derivation of feature vector.
Figure 2
Figure 2
Flowchart of the current method.
Figure 3
Figure 3
The relationship between the accuracy and the number of features.
Figure 4
Figure 4
The relationship between the number of features and the number of generations.
Figure 5
Figure 5
Fitness values and overall accuracy based on the most fitted member of each generation.
Figure 6
Figure 6
Composition of the optimized features subset.
Figure 7
Figure 7
Comparison of different method by the jackknife test at family level.
See this image and copyright information in PMC

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