Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.
Methods Mol Biol. 2022;2412:413-423. doi: 10.1007/978-1-0716-1892-9_21.
Structural vaccinology involves characterizing the interactions between an antigen and antibodies or host immune receptors. Central to this is the task of epitope prediction, which involves describing the binding affinity and interactions of a given peptide typically to the major histocompatibility complex in the case of T-cells or to the antibodies in the case of B-cells. Several computational models exist for this purpose which we will review here. Generally, epitope predictions for MHC-I and MHC-II are substantially different tasks as well as epitope prediction for continuous versus discontinuous B-cell epitopes. Overall, these models suffer from overprediction of epitopes although general themes support both the use of neural networks as well as the incorporation of more abundant and more varied experimental annotation into model training as valuable in improving predictive performance.
结构疫苗学涉及描述抗原与抗体或宿主免疫受体之间的相互作用。这方面的核心任务是表位预测，涉及描述给定肽与主要组织相容性复合体（MHC）的结合亲和力和相互作用，在 T 细胞的情况下是 MHC-I，在 B 细胞的情况下是 MHC-II。为此目的存在几种计算模型，我们将在此进行回顾。一般来说，MHC-I 和 MHC-II 的表位预测是截然不同的任务，以及连续和不连续 B 细胞表位的预测也是不同的。总体而言，这些模型存在过度预测表位的问题，尽管一些普遍的主题支持使用神经网络以及将更丰富和更多样化的实验注释纳入模型训练，这对于提高预测性能是有价值的。
