Evidence map›Paper›PMID 33647011›Full record

ArticlePLoS computational biology2021

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality.

Matthew I J Raybould, Claire Marks, Aleksandr Kovaltsuk, Alan P Lewis, Jiye Shi, Charlotte M Deane

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

19 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025
    Article
  3. Article
  4. Article
  5. Building Representation Learning Models for Antibody Comprehension.Cold Spring Harbor perspectives in biology · 2024
    Review
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  7. Article
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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

6 authors at 3 institutions in 1 country.

Matthew I J RaybouldOxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-5663-5297
Claire MarksOxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-5931-2437
Aleksandr KovaltsukOxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-3806-8302
Alan P LewisData and Computational Sciences, GlaxoSmithKline Research and Development, Stevenage, United Kingdom.
Jiye ShiChemistry Department, UCB Pharma, Slough, United Kingdom.ORCID 0000-0002-9628-8680
Charlotte M DeaneOxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.
University of Oxford · GBGlaxoSmithKline (United Kingdom) · GBUCB Pharma (United Kingdom) · GB

Funding

Biotechnology and Biological Sciences Research Council BB/M011224/1Medical Research Council
6 · The paper itself

Abstract

The naïve antibody/B-cell receptor (BCR) repertoires of different individuals ought to exhibit significant functional commonality, given that most pathogens trigger an effective antibody response to immunodominant epitopes. Sequence-based repertoire analysis has so far offered little evidence for this phenomenon. For example, a recent study estimated the number of shared ('public') antibody clonotypes in circulating baseline repertoires to be around 0.02% across ten unrelated individuals. However, to engage the same epitope, antibodies only require a similar binding site structure and the presence of key paratope interactions, which can occur even when their sequences are dissimilar. Here, we search for evidence of geometric similarity/convergence across human antibody repertoires. We first structurally profile naïve ('baseline') antibody diversity using snapshots from 41 unrelated individuals, predicting all modellable distinct structures within each repertoire. This analysis uncovers a high (much greater than random) degree of structural commonality. For instance, around 3% of distinct structures are common to the ten most diverse individual samples ('Public Baseline' structures). Our approach is the first computational method to find levels of BCR commonality commensurate with epitope immunodominance and could therefore be harnessed to find more genetically distant antibodies with same-epitope complementarity. We then apply the same structural profiling approach to repertoire snapshots from three individuals before and after flu vaccination, detecting a convergent structural drift indicative of recognising similar epitopes ('Public Response' structures). We show that Antibody Model Libraries derived from Public Baseline and Public Response structures represent a powerful geometric basis set of low-immunogenicity candidates exploitable for general or target-focused therapeutic antibody screening.

Indexed as

AntibodiesAntibody DiversityB-LymphocytesDatabases, GeneticImmunodominant EpitopesComputational BiologyHumansAntibodiesImmunodominant Epitopes

Identifiers

PMID33647011
PMCPMC7951972
OpenAlexW3134095091

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.