Evidence map›Paper›PMID 40445971›Full record

ArticlePLoS computational biology2025

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

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

Abstract readComparative Study
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Katharina WauryDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-8570-7640
Stefan LelieveldENPICOM B.V., 's-Hertogenbosch, The Netherlands.
Sanne AbelnDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-2779-7174
Henk-Jan van den HamENPICOM B.V., 's-Hertogenbosch, The Netherlands.ORCID 0000-0001-8582-5404

Funding

European Union’s Horizon 2020
6 · The paper itself

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.

Indexed as

AntibodiesSequence Analysis, ProteinAlgorithmsAmino Acid SequenceCluster AnalysisComputational BiologyComputer SimulationHumansAntibodies

Identifiers

PMID40445971
PMCPMC12148228

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.