Cui Weiren, Chen Lei, Huang Tao, Gao Qian, Jiang Min, Zhang Ning, Zheng Lulu, Feng Kaiyan, Cai Yudong, Wang Hongwei
Center for Translational Medicine and Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210093, China.
Mol Biosyst. 2013 Jun;9(6):1447-52. doi: 10.1039/c3mb70024k. Epub 2013 Mar 21.
Virulence factors are molecules that play very important roles in enhancing the pathogen's capability in causing diseases. Many efforts were made to investigate the mechanism of virulence factors using in silico methods. In this study, we present a novel computational method to predict virulence factors by integrating protein-protein interactions in a STRING database and biological pathways in the KEGG. Three specific species were studied according to their records in the VFDB. They are Campylobacter jejuni NCTC 11168, Escherichia coli O6 : K15 : H31 536 (UPEC) and Pseudomonas aeruginosa PAO1. The prediction accuracies reached were 0.9467, 0.9575 and 0.9180, respectively. Metabolism pathways, flagellar assembly and chemotaxis may be of importance for virulence based on the analysis of the optimal feature sets we obtained. We hope this can provide some insight and guidance for related research.
毒力因子是在增强病原体致病能力方面发挥非常重要作用的分子。人们进行了许多努力，使用计算机方法研究毒力因子的机制。在本研究中，我们提出了一种新的计算方法，通过整合STRING数据库中的蛋白质-蛋白质相互作用和KEGG中的生物途径来预测毒力因子。根据它们在VFDB中的记录研究了三种特定的物种。它们是空肠弯曲菌NCTC 11168、大肠杆菌O6 : K15 : H31 536（UPEC）和铜绿假单胞菌PAO1。达到的预测准确率分别为0.9467、0.9575和0.9180。基于对我们获得的最佳特征集的分析可知，代谢途径、鞭毛组装和趋化作用可能对毒力很重要。我们希望这能为相关研究提供一些见解和指导。
