title: A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies with support vector machines.
abstract: G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, P.R. China.
authors.forename: Guoping
authors.initials: G
authors.lastname: Nie
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, P.R. China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Li
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, P.R. China.
authors.forename: Feichi
authors.initials: F
authors.lastname: Wang
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, P.R. China.
authors.forename: Siwen
authors.initials: S
authors.lastname: Wang
authors.affiliation: College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, P.R. China.
authors.forename: Xuehai
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D017709:Fractals; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D043562:Receptors, G-Protein-Coupled
keywords: GPCR; chaos game representation; fractal dimension; support vector machine