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Record W3179425034 · doi:10.3390/covid1010011

Deactivation of SARS-CoV-2 via Shielding of Spike Glycoprotein Using Carbon Quantum Dots: Bioinformatic Perspective

2021· article· en· W3179425034 on OpenAlexaff
Zahra Ramezani, Mohammad Saaid Dayer, Siamak Noorizadeh, Michael Thompson

Bibliographic record

VenueCOVID · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDocking (animal)ChemistryReceptorMolecular dynamicsBiophysicsVirusStackingMoleculeCombinatorial chemistryVirologyBiochemistryBiologyComputational chemistryMedicineOrganic chemistry

Abstract

fetched live from OpenAlex

The interaction of the spike (S) glycoprotein of SARS-CoV-2 with angiotensin-converting enzyme 2 (ACE2) correlates with increased virus transmissibility and disease severity in humans. Two strategies may be considered for preventive or treatment purposes: the blockage of the ACE2 receptors or the shielding of receptor-binding domains (RBD) in the Sprotein of COVID-19, as well as the S2 cleavage site that is used by the furin enzyme of the host cells in the late phase of virus activation. Herein, the interaction of carbon quantum dots (CQDs) with the Sprotein of SARS-CoV-2 was investigated using molecular docking and molecular dynamics. CQD molecules were optimized by the HF/3-21G level of theory; the probable interactions between the CQDs with Sprotein were studied by blind docking mode, considering the Sprotein as the receptor and CQDs as ligands. Ethanol, folic acid, Favipiravir, two kinds of functionalized triangular hexagonal graphene, and four kinds of functionalized CQDs were studied on a comparative basis. The results show that OH and amine-functionalized CQDs tend to interact with three branches of Sprotein, especially RBD. The fact that they can block the S2 cleavage site leads to their potential use as a therapeutic agent.

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 score0.018
Threshold uncertainty score0.440

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 head0.057
GPT teacher head0.330
Teacher spread0.273 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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