Identification of Potential Universal Vaccine Candidates against Group A Streptococcus by Using High Throughput in Silico and Proteomics Approach

Streptococcus pyogenes or group A Streptococcus (GAS) causes ∼700 million human infections each year, resulting in over 500 000 deaths. The development of a commercial GAS vaccine is hampered due to high strain and serotype diversity in different geographical regions, and the generation of cross-reactive antibodies that may induce autoimmune disease. There is an urgent need to search for alternative vaccine candidates. High throughput multigenome data mining coupled with proteomics seems to be a promising approach to identify the universal vaccine candidates. In the present study, in silico analysis led to prediction of 147 proteins as universal vaccine candidates. Distribution pattern of these predicted candidates was explored in nonsequenced Indian GAS strains (<i>n</i> = 20) by using DNA array hybridization validating in silico analysis. High throughput analyses of surface proteins using 1D-SDS-PAGE coupled with ESI–LC–MS/MS was applied on highly (M49) and less (M1) invasive GAS strains of Indian origin. Comparative proteomics analysis revealed that highly invasive GAS M49 had metabolically more active membrane associated protein machinery than less invasive M1. Further, by overlapping proteomics data with in silico predicted vaccine candidate genes, 52 proteins were identified as probable universal vaccine candidates, which were expressed in these GAS serotypes. These proteins can further be investigated as universal vaccine candidates against GAS. Moreover, this robust approach may serve as a model that can be applied to identify the universal vaccine candidates in case of other pathogenic bacteria with high strain and genetic diversity