主要组织相容性复合体
肽
抗原
MHC I级
生物
MHC限制
T细胞受体
细胞生物学
免疫系统
计算生物学
T细胞
生物化学
免疫学
作者
Bingxu Liu,Nathan F. Greenwood,Julia E. Bonzanini,Amir Motmaen,Jeremy Meyerberg,Tao Dao,Xinyu Xiang,Russell Ault,Jazmin Sharp,Chunyu Wang,Gian Marco Visani,Dionne K. Vafeados,Nicole Roullier,Armita Nourmohammad,David A. Scheinberg,K. Christopher García,David A. Baker
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2025-07-24
卷期号:389 (6758): 386-391
被引量:89
标识
DOI:10.1126/science.adv0185
摘要
Abstract Class I MHC molecules present peptides derived from intracellular antigens on the cell surface for immune surveillance, and specific targeting of these peptide-MHC (pMHC) complexes could have considerable utility for treating diseases. Such targeting is challenging as it requires readout of the few outward facing peptide antigen residues and the avoidance of extensive contacts with the MHC carrier which is present on almost all cells. Here we describe the use of deep learning-based protein design tools to de novo design small proteins that arc above the peptide binding groove of pMHC complexes and make extensive contacts with the peptide. We identify specific binders for ten target pMHCs which when displayed on yeast bind the on-target pMHC tetramer but not closely related peptides. For five targets, incorporation of designs into chimeric antigen receptors leads to T-cell activation by the cognate pMHC complexes well above the background from complexes with peptides derived from proteome. Our approach can generate high specificity binders starting from either experimental or predicted structures of the target pMHC complexes, and should be widely useful for both protein and cell based pMHC targeting.
科研通智能强力驱动
Strongly Powered by AbleSci AI