Computationally identifying virulence factors based on KEGG pathways.

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.