Evidence map›Paper›PMID 42282627›Full record

ArticlebioRxiv : the preprint server for biology2026

Systems-Scale Structural Modeling Reveals the Germline Architecture of Immunodominance.

Ellen L Shrock, Eric Sun, Amrita Dhindsa, Max H Witwer, Zhou Yu, Danton Ivanochko, I-Hsiu Lee, Brian Coventry, Maria Borras Gonzalez, Jean-Philippe Julien and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Ellen L ShrockInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0003-2484-4374
Eric SunInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA.
Amrita DhindsaProgram in Molecular Medicine, The Hospital for Sick Children Research Institute, Toronto, ON M5G 0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada.
Max H WitwerInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA.
Zhou YuDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Danton IvanochkoProgram in Molecular Medicine, The Hospital for Sick Children Research Institute, Toronto, ON M5G 0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada.
I-Hsiu LeeDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Brian CoventryInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA.
Maria Borras GonzalezInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA.
Jean-Philippe JulienProgram in Molecular Medicine, The Hospital for Sick Children Research Institute, Toronto, ON M5G 0A4, Canada; Department of Immunology, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada.
Stephen J ElledgeDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0001-7923-6283
David BakerInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA.

Funding

Resource for Biocomputing Visualization and InformaticsP41GM103311 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2012 to 2017
$8.2M
MATCHMAKERSOT2CA297288 · NCI · UNIVERSITY OF WASHINGTON · PI DAVID BAKER · 2024 to 2026
$1.2M
NCI NIH HHS OT2 CA297288NIGMS NIH HHS P41 GM103311
6 · The paper itself

Abstract

The adaptive immune system generates diverse antibodies to protect against infection, yet responses often focus on a limited number of antigenic sites, a phenomenon called immunodominance. Using the SARS-CoV-2 receptor-binding domain as a model, this study combines large-scale antibody sequencing, deep mutational scanning, and AlphaFold 3 structural modeling to investigate the basis of immunodominant epitope selection. The results show that germline-encoded antibody features are a primary driver of immunodominance. Specifically, 76% of RBD-targeting antibodies display conserved gene segment usage and/or germline-encoded HCDR3 motifs. Structural analyses identified recurrent germline-encoded residues within these regions that interact with immunodominant epitopes, and mutating these residues eliminated binding. Mutations in SARS-CoV-2 variants frequently disrupt these interactions and are associated with immune escape; other mutations enable germline-mediated recognition and generate new immunodominant epitopes. These findings indicate that innate features, rather than diverse somatic mutations, determine binding specificity for the large majority of the antibody response and underlie antibody immunodominance.

Indexed as

Antibodygermline-encoded recognitionimmunodominancesystems immunology

Identifiers

PMID42282627
PMCPMC13252370

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.