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Record W2000778260 · doi:10.1021/ci4003969

Detailed Computational Study of the Active Site of the Hepatitis C Viral RNA Polymerase to Aid Novel Drug Design

2013· article· en· W2000778260 on OpenAlexafffund
Khaled Barakat, John Law, Alessio Prunotto, Wendy C. Magee, David H. Evans, D. Lorne Tyrrell, Jack A. Tuszyński, Michael Houghton

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2013
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of Alberta
FundersCanada Research ChairsCanadian Association of GastroenterologyCanadian Institutes of Health ResearchAlberta InnovatesWestern Canada Research Grid
KeywordsNS5BHepatitis C virusPolymeraseComputational biologyVirologyMolecular mechanicsDrug discoveryActive siteBiologyChemistryEnzymeVirusMolecular dynamicsHepacivirusBioinformaticsBiochemistryComputational chemistry

Abstract

fetched live from OpenAlex

The hepatitis C virus (HCV) RNA polymerase, NS5B, is a leading target for novel and selective HCV drug design. The enzyme has been the subject of intensive drug discovery aimed at developing direct acting antiviral (DAA) agents that inhibit its activity and hence prevent the virus from replicating its genome. In this study, we focus on one class of NS5B inhibitors, namely nucleos(t)ide mimetics. Forty-one distinct nucleotide structures have been modeled within the active site of NS5B for the six major HCV genotypes. Our comprehensive modeling protocol employed 287 different molecular dynamics simulations combined with the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) methodology to rank and analyze these structures for all genotypes. The binding interactions of the individual compounds have been investigated and reduced to the atomic level. The present study significantly refines our understanding of the mode of action of NS5B-nucleotide-inhibitors, identifies the key structural elements necessary for their activity, and implements the tools for ranking the potential of additional much needed novel inhibitors of NS5B.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.157

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 head0.036
GPT teacher head0.300
Teacher spread0.264 · 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

Citations27
Published2013
Admission routes2
Has abstractyes

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