主要组织相容性复合体
生物信息学
MHC I级
肽
计算生物学
管道(软件)
班级(哲学)
MHC II级
计算机科学
T细胞受体
MHC限制
川东北74
生物
T细胞
抗原
免疫学
人工智能
生物化学
基因
免疫系统
程序设计语言
作者
Victor Mikhaylov,Arnold J. Levine
标识
DOI:10.1101/2023.03.06.531396
摘要
Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T-cell surveillance. Reliable in silico prediction of which peptides would be presented and which T-cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling precision and class II peptide register prediction. We explore applications of this method towards improving peptide-MHC binding prediction.
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