Can we predict T cell specificity with digital biology and machine learning?
Can we predict T cell specificity with digital biology and machine learning?
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DOI：
10.1038/s41577-023-00835-3
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发表时间：
2023-08
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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难以解决的问题提供了突破性的解决方案。然而，尽管T细胞受体（TCR）在协调健康和疾病中的细胞免疫中起着关键作用，但从TCR到其同源抗原的可靠图谱的计算重建仍然是系统免疫学的圣杯。目前的数据集仅限于可能的tcr配体对的极小部分，当应用于这些已知的结合剂之外时，最先进的预测模型的性能会减弱。在这篇展望文章中，我们提出了更新和协调跨学科努力的案例，以解决预测tcr抗原特异性的问题。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，并讨论了数字生物学（如单细胞技术和机器学习）的最新进展如何提供可能的解决方案。最后，我们描述了如何预测TCR特异性可能有助于我们理解抗原免疫原性的更广泛的谜题。Koohy和同事们讨论了我们必须如何转向机器学习方法来定义数百万种可能的T细胞受体的抗原特异性。他们回顾了目前用于预测孤儿T细胞受体同源抗原的模型和方法。
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.
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