Che Yuxin, Ju Ying, Xuan Ping, Long Ren, Xing Fei
School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.
School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
PLoS One. 2016 Apr 14;11(4):e0153503. doi: 10.1371/journal.pone.0153503. eCollection 2016.
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/.
酶是参与几乎所有活跃细胞过程的重要且有效的生物催化蛋白。识别多功能酶对于理解酶的功能至关重要。机器学习方法在蛋白质结构和功能预测方面比传统的生物湿实验表现更好。因此，在本研究中，我们探索一种高效且有效的机器学习方法，根据酶的功能对其进行分类。多功能酶采用一种特殊的机器学习策略进行预测，即多标签分类器。序列特征通过自协方差变换从位置特异性评分矩阵中提取。实验结果表明，所提出的方法通过五次交叉验证测试在对六个主要功能类别进行分类时获得了94.1%的准确率，并且优于现有方法。此外，在多功能酶预测中实现了91.25%的准确率，这在其他酶功能预测研究中常常被忽视。可通过链接http://server.malab.cn/MEC/访问在线预测服务器和数据集。
