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A new way of characterizing a person’s immune repertoire after tuberculosis infection could lead to better vaccine designs.

Vaccine development for diseases such as tuberculosis, HIV, malaria and cancers has been hampered by the sheer complexity of T-cell functions and the huge number of antigen specificities.

Now, a team that included an infectious disease expert from the University of Cape Town has developed a computational method for analysing the receptors on the surfaces of T cells that help recognize parts of pathogen invaders. Known as GLIPH2, the freely available tool predicts the specificity of novel T-cell receptors (TCRs) based on genetic sequence similarity with known ones.

When the researchers applied their predictive algorithm to TCR sequence data from 58 individuals latently infected with the bacterium that causes tuberculosis, the tool sorted more than 19,000 unique sequences into TCR groups according to antigen specificity.