T 细胞受体 (TCR) 对主要组织相容性复合体蛋白呈递的外源肽的识别是触发针对病原体或癌症的适应性免疫反应的重要事件。 TCR-肽相互作用的预测对于癌症以及传染病和自身免疫性疾病的治疗非常重要，但仍然是一个重大挑战，特别是对于新的（看不见的）肽表位。在这里，我们提出了 TCRen，一种基于结构的方法，用于对给定 TCR 的候选未见表位进行排名。 TCRen 流程的第一阶段是对 TCR 肽主要组织相容性复杂结构进行建模。然后从该结构中提取 TCR-肽残基接触图，并用于根据与目标 TCR 的相互作用评分对所有候选表位进行排序。使用源自现有晶体结构中 TCR 肽接触偏好统计数据的能量势进行评分。我们证明 TCRen 在区分同源肽和不相关肽方面具有高性能，并且可以促进肿瘤浸润淋巴细胞识别的癌症新表位的识别。© 2024。作者获得 Springer Nature America, Inc. 的独家许可。

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.