t细胞受体洞察:主要组织相容性复合体I类与II类识别的决定因素。

IF 5.2 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Protein Science Pub Date : 2025-09-01 DOI:10.1002/pro.70262
Marcus De Almeida Mendes, Leila Chihab, Jonas Birkelund Nilsson, Lonneke Scheffer, Morten Nielsen, Bjoern Peters
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引用次数: 0

摘要

在本研究中,我们分析了大规模t细胞受体(TCR)序列数据,以确定TCR是否优先结合主要组织相容性复合体(MHC) I类(CD8+)或II类(CD4+)表位。利用国际免疫遗传学信息系统编号方案,我们确定了每个MHC类具有不同氨基酸富集的特定位置,并开发了机器学习模型进行分类。虽然我们基于频率的方法在交叉验证中有效地区分了MHC-I和MHC-II tcr,但当仅使用来自真实外周血单个核细胞样本的β链数据时,性能下降。然而,纳入TCR α链可显著提高准确性,强调其对MHC识别的重要性。总的来说,我们发现v区环可以表明MHC类别偏倚,有助于免疫疗法设计和TCR库分析,同时强调需要更大,更多样化的数据集来进行可靠的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
T-cell receptor insights: Determinants of Major Histocompatibility Complex class I versus class II recognition.

In this study, we analyzed large-scale T-cell receptor (TCR) sequence data to determine whether TCRs preferentially bind to major histocompatibility complex (MHC) class I (CD8+) or class II (CD4+) epitopes. Using the International ImMunoGeneTics information system numbering scheme, we identified specific positions with distinct amino acid enrichment for each MHC class and developed machine learning models for classification. While our frequency-based approach effectively differentiated MHC-I from MHC-II TCRs in cross-validation, performance declined when only beta chain data were used from real-world peripheral blood mononuclear cell samples. However, incorporating the TCR alpha chain significantly improved accuracy, emphasizing its importance for MHC recognition. Overall, we found that V-region loops can signal MHC class bias, aiding in immunotherapy design and TCR repertoire analysis, while highlighting the need for larger, more diverse datasets for reliable predictions.

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来源期刊
Protein Science
Protein Science 生物-生化与分子生物学
CiteScore
12.40
自引率
1.20%
发文量
246
审稿时长
1 months
期刊介绍: Protein Science, the flagship journal of The Protein Society, is a publication that focuses on advancing fundamental knowledge in the field of protein molecules. The journal welcomes original reports and review articles that contribute to our understanding of protein function, structure, folding, design, and evolution. Additionally, Protein Science encourages papers that explore the applications of protein science in various areas such as therapeutics, protein-based biomaterials, bionanotechnology, synthetic biology, and bioelectronics. The journal accepts manuscript submissions in any suitable format for review, with the requirement of converting the manuscript to journal-style format only upon acceptance for publication. Protein Science is indexed and abstracted in numerous databases, including the Agricultural & Environmental Science Database (ProQuest), Biological Science Database (ProQuest), CAS: Chemical Abstracts Service (ACS), Embase (Elsevier), Health & Medical Collection (ProQuest), Health Research Premium Collection (ProQuest), Materials Science & Engineering Database (ProQuest), MEDLINE/PubMed (NLM), Natural Science Collection (ProQuest), and SciTech Premium Collection (ProQuest).
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