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Record W4308210367 · doi:10.1038/s42003-022-04160-8

Antigenic mapping reveals sites of vulnerability on α-HCoV spike protein

2022· article· en· W4308210367 on OpenAlexaff
Jiangchao Xiang, Jie Su, Qiaoshuai Lan, Wenwen Zhao, Yu Zhou, Youwei Xu, Jun Niu, Shuai Xia, Qilian Qi, Sachdev S. Sidhu, Lu Lu, Shane Miersch, Bei Yang

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Waterloo
FundersShanghaiTech UniversityMinistry of Science and Technology of the People's Republic of ChinaShanghai Municipal Education CommissionNational Natural Science Foundation of China
KeywordsEpitopeEpitope mappingAntigenVirologyBiologyAntibodyComputational biologyImmunology

Abstract

fetched live from OpenAlex

Understanding the antigenic signatures of all human coronaviruses (HCoVs) Spike (S) proteins is imperative for pan-HCoV epitopes identification and broadly effective vaccine development. To depict the currently elusive antigenic signatures of α-HCoVs S proteins, we isolated a panel of antibodies against the HCoV-229E S protein and characterized their epitopes and neutralizing potential. We found that the N-terminal domain of HCoV-229E S protein is antigenically dominant wherein an antigenic supersite is present and appears conserved in HCoV-NL63, which holds potential to serve as a pan-α-HCoVs epitope. In the receptor binding domain, a neutralizing epitope is captured in the end distal to the receptor binding site, reminiscent of the locations of the SARS-CoV-2 RBD cryptic epitopes. We also identified a neutralizing antibody that recognizes the connector domain, thus representing the first S2-directed neutralizing antibody against α-HCoVs. The unraveled HCoVs S proteins antigenic similarities and variances among genera highlight the challenges faced by pan-HCoV vaccine design while supporting the feasibility of broadly effective vaccine development against a subset of HCoVs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.146
GPT teacher head0.418
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2022
Admission routes1
Has abstractyes

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