tcrBLOSUM: an amino acid substitution matrix for sensitive alignment of distant epitope-specific TCRs.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-11-22 DOI:10.1093/bib/bbae602
Anna Postovskaya, Koen Vercauteren, Pieter Meysman, Kris Laukens
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Abstract

Deciphering the specificity of T-cell receptor (TCR) repertoires is crucial for monitoring adaptive immune responses and developing targeted immunotherapies and vaccines. To elucidate the specificity of previously unseen TCRs, many methods employ the BLOSUM62 matrix to find TCRs with similar amino acid (AA) sequences. However, while BLOSUM62 reflects the AA substitutions within conserved regions of proteins with similar functions, the remarkable diversity of TCRs means that both TCRs with similar and dissimilar sequences can bind the same epitope. Therefore, reliance on BLOSUM62 may bias detection towards epitope-specific TCRs with similar biochemical properties, overlooking those with more diverse AA compositions. In this study, we introduce tcrBLOSUMa and tcrBLOSUMb, specialized AA substitution matrices for CDR3 alpha and CDR3 beta TCR chains, respectively. The matrices reflect AA frequencies and variations occurring within TCRs that bind the same epitope, revealing that both CDR3 alpha and CDR3 beta display tolerance to a wide range of AA substitutions and differ noticeably from the standard BLOSUM62. By accurately aligning distant TCRs employing tcrBLOSUMb, we were able to improve clustering performance and capture a large number of epitope-specific TCRs with diverse AA compositions and physicochemical profiles overlooked by BLOSUM62. Utilizing both the general BLOSUM62 and specialized tcrBLOSUM matrices in existing computational tools will broaden the range of TCRs that can be associated with their cognate epitopes, thereby enhancing TCR repertoire analysis.

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tcrBLOSUM:用于远距离表位特异性 TCR 敏感比对的氨基酸替代矩阵。
破解 T 细胞受体 (TCR) 复合物的特异性对于监测适应性免疫反应以及开发靶向免疫疗法和疫苗至关重要。为了阐明以前从未见过的 TCR 的特异性,许多方法都采用 BLOSUM62 矩阵来寻找具有相似氨基酸(AA)序列的 TCR。然而,虽然 BLOSUM62 反映了具有相似功能的蛋白质保守区域内的 AA 替换,但 TCR 的显著多样性意味着具有相似和不相似序列的 TCR 都能结合相同的表位。因此,依赖 BLOSUM62 可能会偏向于检测具有相似生化特性的表位特异性 TCR,而忽略那些 AA 组成更多样化的 TCR。在这项研究中,我们引入了 tcrBLOSUMa 和 tcrBLOSUMb,它们分别是 CDR3 alpha 和 CDR3 beta TCR 链的专用 AA 替换矩阵。这两个矩阵反映了结合相同表位的 TCR 中出现的 AA 频率和变化,揭示了 CDR3 alpha 和 CDR3 beta 对广泛的 AA 替换具有耐受性,与标准 BLOSUM62 有明显不同。通过使用 tcrBLOSUMb 对遥远的 TCR 进行精确配准,我们提高了聚类性能,并捕获了 BLOSUM62 忽略的大量具有不同 AA 组成和理化特征的表位特异性 TCR。在现有的计算工具中同时使用通用的 BLOSUM62 和专用的 tcrBLOSUM 矩阵将扩大 TCR 与其同源表位相关联的范围,从而加强 TCR 基因库分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
期刊最新文献
AutoXAI4Omics: an automated explainable AI tool for omics and tabular data. MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics. tcrBLOSUM: an amino acid substitution matrix for sensitive alignment of distant epitope-specific TCRs. A versatile pipeline to identify convergently lost ancestral conserved fragments associated with convergent evolution of vocal learning. ADM: adaptive graph diffusion for meta-dimension reduction.
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