New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome

Abstract

At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T-cell immunity, there is a need for robust tools to enable accurate predictions of peptide–major histocompatibility complex (pMHC) and peptide–MHC–T-cell receptor binding. Improvements in the curation of data sets from high throughput pMHC analysis, such as the NIH Immune Epitope Database (IEDB), and the associated developments of predictive tools rooted in machine-learning approaches, are having significant impact. When such approaches are linked to the powerful empirical immunopeptidome data sets from peptide MHC elution and mass spectrometry, there is considerable potential for rapid translation to T-cell therapies and vaccines.