Evidence map›Paper›PMID 35397794›Full record

ArticleImmunity2022

A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2.

Yiquan Wang, Meng Yuan, Huibin Lv, Jian Peng, Ian A Wilson, Nicholas C Wu

Abstract read
In one paragraph

Article in Immunity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed
–field-weighted citation impact
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

68 citing papers in PubMed.

  1. Trial
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  10. Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Is the vaccination-induced B cell receptor repertoire predictable?Immunoinformatics (Amsterdam, Netherlands) · 2025
    Article
  17. Article
  18. Review
  19. Article
  20. Review

8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yiquan WangDepartment of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Meng YuanDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
Huibin LvDepartment of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Jian PengDepartment of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Ian A WilsonDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
Nicholas C WuDepartment of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. Electronic address: nicwu@illinois.edu.

Funding

Sequence-function relationship of influenza broadly neutralizing antibodiesR01AI167910 · NIAID · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Nicholas C. Wu · 2022 to 2026
$2.5M
High-throughput identification of antibody features for sequence-based epitope predictionDP2AT011966 · NCCIH · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI WU, NICHOLAS C. · 2021 to 2024
$2.4M
MECHANISTIC UNDERSTANDING OF INFLUENZA-HOST INTERACTIONS FROM A ZOONOTIC PERSPECTIVER00AI139445 · NIAID · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI WU, NICHOLAS C. · 2020 to 2021
$492k
NCCIH NIH HHS DP2 AT011966NIAID NIH HHS R00 AI139445NIAID NIH HHS R01 AI167910
6 · The paper itself

Abstract

Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.

Indexed as

COVID-19SARS-CoV-2Antibodies, NeutralizingAntibodies, ViralAntibody FormationHumansPandemicsSpike Glycoprotein, CoronavirusAntibodies, NeutralizingAntibodies, ViralSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2affinity maturationantibodyCOVID-19data miningdeep learningpublic antibody responseSARS-CoV-2sequence analysissomatic hypermutationstructural analysis

Identifiers

PMID35397794
PMCPMC8947961

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.