title: GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble.
abstract: G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at http://111.68.99.218/gpcr-mpredictor/.
authors.affiliation: Department of Computer and Information Science, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Naveed
authors.forename: Asifullah
authors.initials: A
authors.lastname: Khan
authors.forename: Asif Ullah
authors.initials: AU
authors.lastname: Khan
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001499:Bayes Theorem; D030562:Databases, Protein; D020407:Internet; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000145:classification*; D020539:Sequence Analysis, Protein; D012984:Software; D060388:Support Vector Machine
chemical_list: D043562:Receptors, G-Protein-Coupled