Chen Xin-Xin, Tang Hua, Li Wen-Chao, Wu Hao, Chen Wei, Ding Hui, Lin Hao
Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
Biomed Res Int. 2016;2016:1654623. doi: 10.1155/2016/1654623. Epub 2016 Jun 29.
Owing to the abuse of antibiotics, drug resistance of pathogenic bacteria becomes more and more serious. Therefore, it is interesting to develop a more reasonable way to solve this issue. Because they can destroy the bacterial cell structure and then kill the infectious bacterium, the bacterial cell wall lyases are suitable candidates of antibacteria sources. Thus, it is urgent to develop an accurate and efficient computational method to predict the lyases. Based on the consideration, in this paper, a set of objective and rigorous data was collected by searching through the Universal Protein Resource (the UniProt database), whereafter a feature selection technique based on the analysis of variance (ANOVA) was used to acquire optimal feature subset. Finally, the support vector machine (SVM) was used to perform prediction. The jackknife cross-validated results showed that the optimal average accuracy of 84.82% was achieved with the sensitivity of 76.47% and the specificity of 93.16%. For the convenience of other scholars, we built a free online server called Lypred. We believe that Lypred will become a practical tool for the research of cell wall lyases and development of antimicrobial agents.
由于抗生素的滥用，病原菌的耐药性变得越来越严重。因此，开发一种更合理的方法来解决这个问题很有意义。细菌细胞壁裂解酶能够破坏细菌细胞结构进而杀死感染性细菌，是合适的抗菌来源候选物。因此，开发一种准确高效的计算方法来预测裂解酶迫在眉睫。基于此考虑，本文通过搜索通用蛋白质资源（UniProt数据库）收集了一组客观严谨的数据，之后使用基于方差分析（ANOVA）的特征选择技术来获取最优特征子集。最后，使用支持向量机（SVM）进行预测。留一法交叉验证结果表明，最优平均准确率达到84.82%，灵敏度为76.47%，特异性为93.16%。为方便其他学者，我们构建了一个名为Lypred的免费在线服务器。我们相信Lypred将成为细胞壁裂解酶研究和抗菌剂开发的实用工具。
