利用 TCRen 基于结构预测 T 细胞受体对未知表位的识别能力

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Vadim K. Karnaukhov, Dmitrii S. Shcherbinin, Anton O. Chugunov, Dmitriy M. Chudakov, Roman G. Efremov, Ivan V. Zvyagin, Mikhail Shugay
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引用次数: 0

摘要

T 细胞受体(TCR)识别由主要组织相容性复合体蛋白呈现的外来肽是触发对病原体或癌症的适应性免疫反应的主要事件。预测 TCR 与多肽的相互作用对癌症、传染病和自身免疫性疾病的治疗具有重要意义,但这仍然是一个重大挑战,尤其是对于新的(未见过的)多肽表位。在此,我们介绍一种基于结构的方法--TCRen,用于对给定 TCR 的候选未见表位进行排序。TCRen 管道的第一阶段是对 TCR-肽-主要组织相容性复合体结构进行建模。然后从该结构中提取 TCR-肽残基接触图,并根据与目标 TCR 的相互作用得分对所有候选表位进行排序。根据现有晶体结构中 TCR-肽接触偏好的统计数据得出的能量势进行评分。我们的研究表明,TCRen 在区分同源肽和非同源肽方面具有很高的性能,有助于识别肿瘤浸润淋巴细胞识别的癌症新表位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen
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. TCRen predicts TCR specificity by modeling the TCR–peptide–MHC structure and estimating the TCR–peptide interaction energy using a statistical potential. The use of structural information allows TCRen to generalize to unseen epitopes, such as cancer neoepitopes.
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CiteScore
11.70
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