MétaCan
Menu
Back to cohort
Record W4226053901 · doi:10.1016/j.celrep.2021.110156

Structural analysis of receptor binding domain mutations in SARS-CoV-2 variants of concern that modulate ACE2 and antibody binding

2021· article· en· W4226053901 on OpenAlexafffund
Dhiraj Mannar, James W. Saville, Xing Zhu, Shanti Swaroop Srivastava, Alison Berezuk, Steven Zhou, Katharine S. Tuttle, Andrew Kim, Wei Li, Dimiter S. Dimitrov, Sriram Subramaniam

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British Columbia
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsMutationAntibodyBiologyReceptorAngiotensin-converting enzyme 2CoronavirusGeneMolecular biologyCell biologyGeneticsVirologyCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

The recently emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Beta (B.1.351) and Gamma (P.1) variants of concern (VoCs) include a key mutation (N501Y) found in the Alpha (B.1.1.7) variant that enhances affinity of the spike protein for its receptor, angiotensin-converting enzyme 2 (ACE2). Additional mutations are found in these variants at residues 417 and 484 that appear to promote antibody evasion. In contrast, the Epsilon variants (B.1.427/429) lack the N501Y mutation yet exhibit antibody evasion. We have engineered spike proteins to express these receptor binding domain (RBD) VoC mutations either in isolation or in different combinations and analyze the effects using biochemical assays and cryoelectron microscopy (cryo-EM) structural analyses. Overall, our findings suggest that the emergence of new SARS-CoV-2 variant spikes can be rationalized as the result of mutations that confer increased ACE2 affinity, increased antibody evasion, or both, providing a framework to dissect the molecular factors that drive VoC evolution.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.365
Teacher spread0.310 · 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

Citations98
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207