CWLy-SVM: A support vector machine-based tool for identifying cell wall lytic enzymes. (August 2020)

• Record Type: Journal Article
• Authors: Meng, Chaolu; Guo, Fei; Zou, Quan
• Is Part Of: Computational biology and chemistry. Volume 87(2020)
• Publication Date: 2020-08
• Subjects: Cell wall lytic enzymes -- Support vector machine -- Bioinformatics -- Machine leaning; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107304
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: We identified cell wall lytic enzymes in bioinformatic way to overcome inefficiency of in vitro experiments and provide a website tool by wrapping the proposed model. Our proposed model outperforms the state-of-the-art method in jackknife cross validation test. We comprehensively analyzed the optimal feature set of proposed model from the prospective of data and biological meaning. Abstract: Cell wall lytic enzymes, as an important biotechnical tool in drug development, agriculture and the food industry, have attracted more research attention. In this research, the accurate identification of cell wall lytic enzymes is one of the key and fundamental tasks. In this study, in order to eliminate the inefficiency of in vitro experiments, a support vector machine-based cell wall lytic enzyme identification model was constructed using bioinformatics. This machine learning process includes feature extraction, feature selection, model training and optimization. According to the jackknife cross validation test, this model obtained a sensitivity of 0.853, a specificity of 0.977, an MCC of 0.845 and an AUC of 0.915. These benchmark results demonstrate that the proposed model outperforms the state-of-the-art method and that it has powerful cell wall lytic enzyme identification ability. Furthermore, we comprehensively analyzed the selected optimal features and used the proposed model to construct a user friendly web server called the CWLy-SVM to identify cell wall lytic enzymes, which is available at http://server.malab.cn/CWLy-SVM/index.jsp .