Evidence map›Paper›PMID 31490930›Full record

ArticlePloS one2019

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes.

Michael L Paull, Tim Johnston, Kelly N Ibsen, Joel D Bozekowski, Patrick S Daugherty

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024
    Review
  6. Article
  7. Article
  8. 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

5 authors at 1 institution in 1 country.

Michael L PaullDepartment of Chemical Engineering, University of California Santa Barbara, California, United States of America.ORCID 0000-0001-8416-3314
Tim JohnstonDepartment of Chemical Engineering, University of California Santa Barbara, California, United States of America.
Kelly N IbsenDepartment of Chemical Engineering, University of California Santa Barbara, California, United States of America.
Joel D BozekowskiDepartment of Chemical Engineering, University of California Santa Barbara, California, United States of America.
Patrick S DaughertyDepartment of Chemical Engineering, University of California Santa Barbara, California, United States of America.
University of California, Santa Barbara · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.

Indexed as

AdolescentAdultAgedAlgorithmsAntibodiesAntigens, BacterialAntigens, ViralChildEnterovirusEpitopesHumansMiddle AgedProteomeProteomicsSequence Analysis, ProteinStaphylococcusAntibodiesAntigens, BacterialAntigens, ViralEpitopesProteome

Identifiers

PMID31490930
PMCPMC6730857
OpenAlexW2971516548

What OpenQuestion holds

Textmetadata
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.