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Pre-print journal club review of BioRxiv article: Functional assessment of cell entry and receptor usage for lineage B β-coronaviruses, including 2019-nCoV

2020· dataset· en· W4206719158 on OpenAlexaff
Craig McCormick

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakLineage (genetic)CoronavirusJournal clubBiologyClubVirologyComputational biologyEvolutionary biologyGeneticsWorld Wide WebMedicineComputer scienceGenePathologyAnatomyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Letko and Munster report a new functional viromics platform whereby receptor binding domains (RBDs) from different lineage B betacoronaviruses were cloned into a codon-optimized gene for SARS CoV spike protein which was then incorporated into pseudotyped VSV particles for functional assays.Entry was indicated by luciferase reporter activity.This screen facilitated rapid identification of RBDreceptor interactions, with less expense than previous methods.The authors confirmed previous findings that only RBDs belonging to clade-1 of the B-lineage of beta-coronaviruses use the ACE2 receptor.Furthermore, the authors showed that for a variety of B-lineage coronaviruses, protease treatment prior to infection enhances entry into different cell types from different species.They confirmed that protease treatment enhanced receptor-dependent viral entry.By introducing 14 amino acids known to contact the ACE2 receptor into clade-2 and clade-3 RBDs, the authors confirmed that these AAs are important for ACE2 recognition.They also determined that the surrounding AA sequence context is important for ACE2 recognition.Finally, they showed that the new 2019-nCoV coronavirus (now known as SARS-CoV-2 according to the ICTV) is related to Clade-1 betacoronaviruses, similar to SARS, and also utilizes the ACE2 entry receptor. OVERALL ASSESSMENT:STRENGTHS: Overall, we conclude that this is a scientifically sound and well-written article by Letko and Munster.The authors' conclusions are generally well-supported by the data.The authors report a screen that is rapid, effective, and cost-efficient compared to previous methods to screen coronavirus receptor usage.The authors were also the first to show that SARS-CoV-2 uses the ACE2 receptor similar to SARS.This demonstrates the effectiveness and rapidity of their screen.The authors confirmed results from previous studies that protease treatment aids viral entry but is not sufficient to promote viral entry into cells that lack cognate receptors.Overall this a strong manuscript that provides important information relevant to the current SARS-CoV-2 outbreak.WEAKNESSES: Some improvements could be made to strengthen the manuscript and provide better support

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.441

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1320.061

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 head0.085
GPT teacher head0.405
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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