生物信息学
T细胞受体
计算生物学
主要组织相容性复合体
生物
集合(抽象数据类型)
比例(比率)
反应性(心理学)
T细胞
生物系统
交叉反应性
生物信息学
计算机科学
灵敏度(控制系统)
作者
Marius Messemaker,Bjørn P. Y. Kwee,Živa Moravec,Sam de Paauw,Jos H. M. Urbanus,Daniel Álvarez Salmoral,Jeroen Geerligs,Ren Xie,Rhianne Voogd,Ben Morris,Aurélie Guislain,Yael Winkler,Connor A. Richterich,Maike Mussmann,Marijn Bakker,Maxime Steinmetz,Matyas Iras,Eric Marcus,Jonas Teuwen,Anastassis Perrakis A
出处
期刊:Immunity
[Cell Press]
日期:2026-10-01
卷期号:59 (10): 2775-2788.e9
被引量:2
标识
DOI:10.1016/j.immuni.2026.09.007
摘要
Accurate prediction of T cell receptor (TCR) reactivity is a long-standing goal in immunology. Here, we asked whether functional validation of TCR-peptide-major histocompatibility complex (MHC) (TCR-pMHC) pairs at scale alters performance estimates of TCR-pMHC reactivity prediction models. We developed a TCR rapid assembly platform (T-RAP) that allowed the generation of large and uniform TCR libraries. T-RAP enabled evaluation of TCR signaling or MHC multimer binding of thousands of previously reported TCR-pMHC pairs under standardized conditions. In this setting of systematic standardized evaluation, only ∼50% of these TCRs showed the previously reported TCR reactivity. Notably, AlphaFold3 structural predictions showed good performance in identifying reactive TCR-pMHC pairs within the set of TCRs that experimentally validated without any task-specific training. AlphaFold3 structural predictions could likewise be used to shortlist TCRs reactive to patient-specific cancer neoantigens. In silico prediction of TCR-pMHC reactivity thus has greater feasibility than previously assumed, with implications for basic research and clinical application.
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