title: Identification of Multi-Functional Enzyme with Multi-Label Classifier.
abstract: Enzymes are important and effective biological catalyst proteins participating in almost all active cell processes. Identification of multi-functional enzymes is essential in understanding the function of enzymes. Machine learning methods perform better in protein structure and function prediction than traditional biological wet experiments. Thus, in this study, we explore an efficient and effective machine learning method to categorize enzymes according to their function. Multi-functional enzymes are predicted with a special machine learning strategy, namely, multi-label classifier. Sequence features are extracted from a position-specific scoring matrix with autocross-covariance transformation. Experiment results show that the proposed method obtains an accuracy rate of 94.1% in classifying six main functional classes through five cross-validation tests and outperforms state-of-the-art methods. In addition, 91.25% accuracy is achieved in multi-functional enzyme prediction, which is often ignored in other enzyme function prediction studies. The online prediction server and datasets can be accessed from the link http://server.malab.cn/MEC/.
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Che
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ju
authors.affiliation: School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Xuan
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Ren
authors.initials: R
authors.lastname: Long
authors.affiliation: School of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D000069550:Machine Learning; D064251:Multifunctional Enzymes / Q000737:chemistry* / Q000145:classification*; D056510:Position-Specific Scoring Matrices
chemical_list: D064251:Multifunctional Enzymes