MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
The Rosalind Franklin Institute, Didcot, UK.
Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK.
Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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.
中文翻译：
机器学习和实验生物学的最新进展为蛋白质结构预测等长期以来被认为棘手的问题提供了突破性的解决方案。然而，尽管 T 细胞受体 (TCR) 在协调健康和疾病的细胞免疫方面发挥着关键作用，计算重建从 TCR 到其同源抗原的可靠图谱仍然是系统免疫学的圣杯。目前的数据集仅限于可能的 TCR-配体对的一小部分，并且当应用超出这些已知结合剂时，最先进的预测模型的性能会减弱。在这篇 Perspective 文章中，我们提出了重新协调跨学科努力以解决预测 TCR 抗原特异性问题的理由。我们列出了抗原结合预测模型的一般要求，强调了关键挑战，并讨论了单细胞技术和机器学习等数字生物学的最新进展如何提供可能的解决方案。最后，我们描述了预测 TCR 特异性如何有助于我们理解更广泛的抗原免疫原性难题。
