Tuberculosis (TB) is the leading infectious cause of death worldwide and claimed over 1.6 million lives in 2017. Furthermore, one-third of the world population is estimated to be latently infected with Mycobacterium tuberculosis (MTB). A safe and effective MTB vaccine that can prevent both the primary infection and the reactivation of latent tuberculosis infection (LTBI), and that can protect against all forms of TB in adults and adolescents is urgently needed. In this study, using computational approaches, we predicted the capacity of the epitopes to be presented by the HLA molecules for ten MTB protein antigens (Mtb39a, Mtb32a, Ag85B, ESAT-6, TB10.4, Rv2660, Rv2608, Rv3619, Rv3620, and Rv1813) constituting five MTB subunit vaccines (M72, H1, H4, H56, and ID93) that are currently in clinical trials. We also assessed the promiscuity of the predicted epitopes based on a reference set of alleles and supertype alleles, and estimated the population coverage of the ten antigens in three high TB burden countries (China, India, and South Africa). Among the ten antigens evaluated, Rv2608 was found to have the highest number of promiscuous epitopes predicted to bind the most MHC-I and MHC-II supertype alleles, highest predicted immunogenicity, and the broadest population coverage in three high burden countries. Between the two latency-related antigens (Rv1813 and Rv2660), Rv1813 was predicted to have a better epitope diversity and promiscuity, immunogenicity, and population coverage. As a result, the ID93 vaccine consisted of Rv2608, Rv1813, Rv3619, and Rv3620 was predicted to have the best potential for preventing both active and latent TB infection. Our results highlighted the importance and usefulness of a systematic and comprehensive assessment of protein antigens using computational approaches in MTB vaccine development.
Keywords: Epitope prediction; Immunogenicity; Population coverage; Promiscuous epitopes; Subunit vaccine; Tuberculosis.
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