EPIC-TRACE:利用注意力和上下文嵌入预测 TCR 与未知表位的结合

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-12-09 DOI:10.1093/bioinformatics/btad743
Dani Korpela, Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Satu Mustjoki, Harri Lähdesmäki
{"title":"EPIC-TRACE:利用注意力和上下文嵌入预测 TCR 与未知表位的结合","authors":"Dani Korpela, Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Satu Mustjoki, Harri Lähdesmäki","doi":"10.1093/bioinformatics/btad743","DOIUrl":null,"url":null,"abstract":"Motivation T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. Results We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. Code availability https://github.com/DaniTheOrange/EPIC-TRACE Supplementary information Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings\",\"authors\":\"Dani Korpela, Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Satu Mustjoki, Harri Lähdesmäki\",\"doi\":\"10.1093/bioinformatics/btad743\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivation T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. Results We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. Code availability https://github.com/DaniTheOrange/EPIC-TRACE Supplementary information Supplementary data are available at Bioinformatics online.\",\"PeriodicalId\":8903,\"journal\":{\"name\":\"Bioinformatics\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btad743\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad743","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

动机 T 细胞在适应性免疫系统中发挥着对抗病原体和癌症的重要作用,但也可能引发自身免疫性疾病。T细胞受体(TCR)识别多肽-MHC(pMHC)复合物是引起免疫反应的必要条件。目前已开发出许多机器学习模型来预测这种结合,但将预测结果推广到训练数据之外的 pMHC 仍然具有挑战性。结果 我们开发了一种新的机器学习模型,它利用了来自 α 和 β 链、表位序列和 MHC 的 TCR 信息。我们的方法使用了针对两条链和表位的氨基酸序列的 ProtBERT 嵌入以及卷积和多头注意力架构。我们展示了每个输入特征的重要性,以及将只有少量 TCR 的表位纳入训练数据的好处。我们在现有数据库上对我们的模型进行了评估,结果表明该模型优于其他最先进的模型。代码可用性 https://github.com/DaniTheOrange/EPIC-TRACE 补充信息 补充数据可在 Bioinformatics online 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings
Motivation T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. Results We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. Code availability https://github.com/DaniTheOrange/EPIC-TRACE Supplementary information Supplementary data are available at Bioinformatics online.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
期刊最新文献
MEHunter: Transformer-based mobile element variant detection from long reads Metabolic syndrome may be more frequent in treatment-naive sarcoidosis patients. Coracle—A Machine Learning Framework to Identify Bacteria Associated with Continuous Variables CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1