Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach.

Tuberculosis (TB) is the leading cause of death of any single infectious agent, having led to 1.4 million deaths in 2019 alone. Moreover, an estimated one-quarter of the global population is latently infected with Mycobacterium tuberculosis (MTB), presenting a huge pool of potential future disease. Nonetheless, the only currently licensed TB vaccine fails to prevent the activation of latent TB infections (LTBI). These facts together illustrate the desperate need for a more effective TB vaccine strategy that can prevent both primary infection and the activation of LTBI. In this study, we employed a machine learning-based reverse vaccinology approach to predict the likelihood that each protein within the proteome of MTB laboratory reference strain H37Rv would be a protective antigen (PAg). The proteins predicted most likely to be a PAg were assessed for their belonging to a protein family of previously established PAgs, the relevance of their biological processes to MTB virulence and latency, and finally the immunogenic potential that they may provide in terms of the number of promiscuous epitopes within each. This study led to the identification of 16 proteins with the greatest vaccine potential for further in vitro and in vivo studies. It also demonstrates the value of computational methods in vaccine development.

Stabilized coronavirus spike stem elicits a broadly protective antibody.

Current coronavirus (CoV) vaccines primarily target immunodominant epitopes in the S1 subunit, which are poorly conserved and susceptible to escape mutations, thus threatening vaccine efficacy. Here, we use structure-guided protein engineering to remove the S1 subunit from the Middle East respiratory syndrome (MERS)-CoV spike (S) glycoprotein and develop stabilized stem (SS) antigens. Vaccination with MERS SS elicits cross-reactive ß-CoV antibody responses and protects mice against lethal MERS-CoV challenge. High-throughput screening of antibody-secreting cells from MERS SS-immunized mice led to the discovery of a panel of cross-reactive monoclonal antibodies. Among them, antibody IgG22 binds with high affinity to both MERS-CoV and severe acute respiratory syndrome (SARS)-CoV-2 S proteins, and a combination of electron microscopy and crystal structures localizes the epitope to a conserved coiled-coil region in the S2 subunit. Passive transfer of IgG22 protects mice against both MERS-CoV and SARS-CoV-2 challenge. Collectively, these results provide a proof of principle for cross-reactive CoV antibodies and inform the development of pan-CoV vaccines and therapeutic antibodies.