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Record W3169696692 · doi:10.3390/v13061055

A Bioluminescent Biosensor for Quantifying the Interaction of SARS-CoV-2 and Its Receptor ACE2 in Cells and In Vitro

2021· article· en· W3169696692 on OpenAlexaff
Xiaolong Yang, Lidong Liu, Yawei Hao, Eva So, Sahar Sarmasti Emami, Derek Zhang, Yanping Gong, Prameet M. Sheth

Bibliographic record

VenueViruses · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsBiosensorMutantIn vitroVirusCoronavirusVirologyReceptorBiologyHigh-throughput screeningSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ChemistryAngiotensin-converting enzyme 2Drug discoveryCoronavirus disease 2019 (COVID-19)Computational biologyBiochemistryGeneMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is currently spreading and mutating with increasing speed worldwide. Therefore, there is an urgent need for a simple, sensitive, and high-throughput (HTP) assay to quantify virus-host interactions in order to quickly evaluate the infectious ability of mutant viruses and to develop or validate virus-inhibiting drugs. Here, we developed an ultrasensitive bioluminescent biosensor to evaluate virus-cell interactions by quantifying the interaction between the SARS-CoV-2 receptor binding domain (RBD) and its cellular receptor angiotensin-converting enzyme 2 (ACE2) both in living cells and in vitro. We have successfully used this novel biosensor to analyze SARS-CoV-2 RBD mutants and evaluated candidate small molecules (SMs), antibodies, and peptides that may block RBD:ACE2 interaction. This simple, rapid, and HTP biosensor tool will significantly expedite the detection of viral mutants and the anti-COVID-19 drug discovery process.

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)0.000
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 score0.034
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.165
GPT teacher head0.411
Teacher spread0.246 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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