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Record W3109013015 · doi:10.1021/acs.jcim.0c00742

Application of Molecular Dynamics Simulations to the Design of Nucleotide Inhibitors Binding to Norovirus Polymerase

2020· article· en· W3109013015 on OpenAlexafffund
Holly Freedman, Juthika Kundu, Egor P. Tchesnokov, John Lok Man Law, James A. Nieman, Raymond F. Schinazi, D. Lorne Tyrrell, Matthias Götte, Michael Houghton

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2020
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsUniversity of Alberta
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchAlberta InnovatesCanada Excellence Research Chairs, Government of Canada
KeywordsNorovirusNucleotidePolymeraseRNA polymeraseRNA-dependent RNA polymeraseMolecular dynamicsVirologyVirtual screeningChemistryRNAComputational biologyBiologyGeneticsDNAVirusGeneComputational chemistry

Abstract

fetched live from OpenAlex

The RNA-dependent RNA polymerase (RdRp) of norovirus is an attractive target of antiviral agents aimed at providing protection against norovirus-associated gastroenteritis. Here, we perform molecular dynamics simulations of the crystal structure of norovirus RdRp in complex with several known binders, as well as free-energy simulations by free-energy perturbation (FEP) to determine binding free energies of these molecules relative to the natural nucleotide substrates. We determine experimental EC50 values and nucleotide incorporation efficiencies for several of these compounds. Moreover, we investigate the mechanism of inhibition of some of these ligands. Using FEP, we screened a virtual nucleotide library with 121 elements for binding to the polymerase and successfully identified two novel chain terminators.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.034
GPT teacher head0.311
Teacher spread0.277 · 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 designSimulation or modeling
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

Citations12
Published2020
Admission routes2
Has abstractyes

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