PMC:5056897 / 8410-9436 JSONTXT

{"project":"2_test","denotations":[{"id":"27729842-18226234-44840767","span":{"begin":385,"end":387},"obj":"18226234"},{"id":"27729842-16689701-44840768","span":{"begin":403,"end":405},"obj":"16689701"}],"text":"Identification of virulence proteins\nThe present work put stress upon the identification of potential virulence proteins in the pool of HPs. Pathogenic bacteria contain a range of virulence proteins in their pathogenesis machinery. There are adhesins, exotoxins, endotoxins, and secretion systems, etc., that comprise the virulence moiety of pathogenic bacteria. We used VirulentPred [32] and VICMpred [33] for the identification of virulence factors among the HPs. Both these tools are Support Vector Machine (SVM) based using 5-fold cross-validation processes to validate the results. VirulentPred uses the strategy of two-way predictions, i.e., non-Virulent or Virulent whereas VICMpred categorizes proteins into four classes namely proteins involved in cellular processes, metabolism protein, information molecule, and virulence factors. It has a training set of 670 proteins from Gram-negative bacteria including 70 known virulence factors. Information for virulence factors analysis is provided in Supplementary Table 3."}