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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

Comparison of sequence- and structure-based antibody clustering approaches on simulated repertoire sequencing data.

作者信息

Waury Katharina, Lelieveld Stefan, Abeln Sanne, van den Ham Henk-Jan

机构信息

Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

AI Technology For Life, Department of Information and Computing Science, and Department of Biology, Utrecht University, Utrecht, The Netherlands.

出版信息

PLoS Comput Biol. 2025 May 30;21(5):e1013057. doi: 10.1371/journal.pcbi.1013057. eCollection 2025 May.

Abstract

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.

摘要

repertoire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流程中的关键步骤,有助于识别功能相关的抗体。传统的克隆型聚类方法依赖于序列信息,特别是CDRH3序列同一性和V/J基因使用情况,将序列分组为克隆型。有人提出,通过使用结构信息对抗体进行分组,可以克服基于序列的方法在识别序列不同但功能趋同的抗体方面的局限性。最近的进展使基于结构的方法在repertoire水平上变得可行。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。缺少对基于结构的工具在现实多样的repertoire数据上的优缺点的全面比较。在这里,我们旨在探索基于结构的聚类算法取代或增强标准基于序列的方法的前景,特别是通过识别低序列同一性组。针对克隆型分析评估了两种方法,SAAB+和SPACE2。我们精心策划了一个数据集,其中包含注释良好的抗体对,这些抗体在表位残基上有高度重叠,因此在各自抗原内结合相同区域。将这组抗体引入模拟的repertoire中,以比较聚类方法在多样抗体组上的性能。我们的分析表明,与克隆型分析相比,基于结构的方法确实能将更多抗体聚集在一起。然而,这也突出了SPACE2对相同长度CDR区域的需求所带来的局限性。这项工作全面比较了不同聚类方法的效用,并深入了解了有效利用抗体结构信息对免疫repertoire数据进行分组还需要采取哪些进一步的步骤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3aa/12148228/b8e2a9617133/pcbi.1013057.g001.jpg

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