基于序列和结构的抗体聚类方法在模拟库测序数据上的比较。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Katharina Waury, Stefan Lelieveld, Sanne Abeln, Henk-Jan van den Ham
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引用次数: 0

摘要

库测序使我们能够研究抗体介导的免疫反应。序列聚类是数据分析管道中至关重要的一步,有助于识别功能相关抗体。传统的聚类克隆分型方法依赖于序列信息,特别是CDRH3序列身份和V/J基因使用情况,将序列划分为克隆型。研究表明,利用结构信息对抗体进行分组可以克服基于序列方法鉴定序列不相似但功能趋同抗体的局限性。最近的进展使得基于结构的方法在曲目水平上可行。然而,到目前为止,它们的性能仅在单抗原抗体组上进行了评估。目前还没有对基于结构的工具在实际和多样化的保留数据上的优势和局限性进行全面的比较。在这里,我们的目标是探索基于结构的聚类算法的前景,以取代或增强标准的基于序列的方法,特别是通过识别低序列的身份群。对SAAB+和SPACE2两种克隆分型方法进行了评价。我们整理了一个标记良好的抗体对的数据集,这些抗体在表位残基上显示出高度重叠,从而在各自的抗原内结合相同的区域。这组抗体被引入到模拟库中,以比较聚类方法在不同抗体集上的性能。我们的分析表明,与克隆分型相比,基于结构的方法确实将更多的抗体分组在一起。然而,这也突出了SPACE2需要相同长度CDR区域的局限性。这项工作彻底比较了不同聚类方法的效用,并提供了有效利用抗体结构信息对免疫库数据进行分组所需的进一步步骤的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of sequence- and structure-based antibody clustering approaches on simulated repertoire sequencing data.

Repertoire sequencing allows us to investigate the antibody-mediated immune response. The clustering of sequences is a crucial step in the data analysis pipeline, aiding in the identification of functionally related antibodies. The conventional clustering approach of clonotyping relies on sequence information, particularly CDRH3 sequence identity and V/J gene usage, to group sequences into clonotypes. It has been suggested that the limitations of sequence-based approaches to identify sequence-dissimilar but functionally converged antibodies can be overcome by using structure information to group antibodies. Recent advances have made structure-based methods feasible on a repertoire level. However, so far, their performance has only been evaluated on single-antigen sets of antibodies. A comprehensive comparison of the benefits and limitations of structure-based tools on realistic and diverse repertoire data is missing. Here, we aim to explore the promise of structure-based clustering algorithms to replace or augment the standard sequence-based approach, specifically by identifying low-sequence identity groups. Two methods, SAAB+ and SPACE2, are evaluated against clonotyping. We curated a dataset of well-annotated pairs of antibodies that show high overlap in epitope residues and thus bind the same region within their respective antigen. This set of antibodies was introduced into a simulated repertoire to compare the performance of clustering approaches on a diverse antibody set. Our analysis reveals that structure-based methods do group more antibodies together compared to clonotyping. However, it also highlights the limitations associated with the need for same-length CDR regions by SPACE2. This work thoroughly compares the utility of different clustering methods and provides insights into what further steps are required to effectively use antibody structural information to group immune repertoire data.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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