Can we predict T cell specificity with digital biology and machine learning? - Hudson, Dan; Fernandes, Ricardo A.; Basham, Mark; Ogg, Graham; Koohy, Hashem (Nature reviews. Immunology, 2023)
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR–ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR–antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity. Koohy and co-workers discuss how we must turn to machine-learning approaches to define the antigen specificity of the many millions of possible T cell receptors. They review the models and methods currently being used to predict cognate antigens for orphan T cell receptors.