テクノロジーと最適化のグローバル ジャーナル

In Silico Identification of Novel Potential Vaccine Candidates in Streptococcus pneumoniae

Abstract

Anjali Wadhwani and Varun Khanna

Currently, most reverse vaccinology studies aim to identify novel proteins with signature motifs commonly found in surface exposed proteins. In the current manuscript, our objective was to computationally identify conserved, antigenic, classically or non-classically secreted proteins in pathogenic strains of Streptococcus pneumoniae. The pathogenic strains used in our analysis were TIGR4, D39, CGSP14, 19A-6, JJA, 70585, AP200, 6706B and TCH8431. PSORTb 3.0.2 was used to infer subcellular locations while SecretomeP 2.0 server was run to predict non-classically secreted proteins. Virulence was predicted using MP3 and VirulentPred webservers. A systematic workflow designed for reverse vaccinology identified 83 (45 classically secreted and 38 non-classically secreted) potential virulence factors. However, many proteins were uncharacterized. Therefore, InterProScan was run for functional annotation. Proteins failing to be annotated were filtered out leaving a set of 24 proteins (9 classically secreted and 15 non-classically secreted) as our final prediction for potential vaccine candidates. Nevertheless, predicted proteins needs to be validated in biological assays before their use as vaccines.