Predicting T cell receptor functionality against mutant epitopes.

IF 11.1 Q1 CELL BIOLOGY
Cell genomics Pub Date : 2024-09-11 Epub Date: 2024-08-15 DOI:10.1016/j.xgen.2024.100634
Felix Drost, Emilio Dorigatti, Adrian Straub, Philipp Hilgendorf, Karolin I Wagner, Kersten Heyer, Marta López Montes, Bernd Bischl, Dirk H Busch, Kilian Schober, Benjamin Schubert
{"title":"Predicting T cell receptor functionality against mutant epitopes.","authors":"Felix Drost, Emilio Dorigatti, Adrian Straub, Philipp Hilgendorf, Karolin I Wagner, Kersten Heyer, Marta López Montes, Bernd Bischl, Dirk H Busch, Kilian Schober, Benjamin Schubert","doi":"10.1016/j.xgen.2024.100634","DOIUrl":null,"url":null,"abstract":"<p><p>Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480844/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2024.100634","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/15 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

预测 T 细胞受体对突变表位的功能。
癌细胞和病原体可通过免疫原表位的突变逃避 T 细胞受体(TCR)。TCR交叉反应(即识别具有序列相似性的多个表位)可以抵消这种逃避,但可能会通过靶向自身抗原在基于细胞的免疫疗法中引起严重的副作用。为了预测表位点突变对 T 细胞功能的影响,我们在此提出了基于随机森林的预测 T 细胞表位特异性激活对抗突变体模型(P-TEAM)。我们在三个数据集上对 P-TEAM 进行了训练和测试,这三个数据集包含了 TCR 对模型表位 SIINFEKL、肿瘤新表位 VPSVWRSSL 和人类巨细胞病毒抗原 NLVPMVATV 的单氨基酸突变的反应,总共有 9690 个独特的 TCR 表位相互作用。P-TEAM 能够对 T 细胞反应性进行准确分类,并定量预测未观察到的单点突变和未见过的 TCR 的 T 细胞功能。总之,P-TEAM 为研究 T 细胞对突变表位的反应提供了有效的计算工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.10
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信