title: Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach.
abstract: Matrix metalloproteinase (MMPs) and disintegrin and metalloprotease (ADAMs) belong to the zinc-dependent metalloproteinase family of proteins. These proteins participate in various physiological and pathological states. Thus, prediction of these proteins using amino acid sequence would be helpful. We have developed a method to predict these proteins based on the features derived from Chou's pseudo amino acid composition (PseAAC) server and support vector machine (SVM) as a powerful machine learning approach. With this method, for ADAMs and MMPs families, an overall accuracy and Matthew's correlation coefficient (MCC) of 95.89 and 0.90% were achieved respectively. Furthermore, the method is able to predict two major subclasses of MMP family; Furin-activated secreted MMPs and Type II trans-membrane; with MCC of 0.89 and 0.91%, respectively. The overall accuracy for Furin-activated secreted MMPs and Type II trans-membrane was 98.18 and 99.07, respectively. Our data demonstrates an effective classification of Metalloproteinase family based on the concept of PseAAC and SVM.
authors.affiliation: Department of Biomedical Engineering, University of Isfahan, Isfahan, Iran.
authors.forename: Majid
authors.initials: M
authors.lastname: Mohammad Beigi
authors.forename: Mohaddeseh
authors.initials: M
authors.lastname: Behjati
authors.forename: Hassan
authors.initials: H
authors.lastname: Mohabatkar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D045726:Metalloproteases / Q000737:chemistry* / Q000145:classification; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D045726:Metalloproteases