MétaCan
Menu
Back to cohort
Record W2558077757 · doi:10.1021/jacs.6b10399

Structural Insight into NS5 of Zika Virus Leading to the Discovery of MTase Inhibitors

2016· article· en· W2558077757 on OpenAlexafffund
Preyesh Stephen, Mariana Baz, Guy Boivin, Sheng‐Xiang Lin

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchFondation CHU de Québec
KeywordsZika virusDruggabilityChemistryFlavivirusVirtual screeningVirologyRNADrug discoveryMethyltransferaseAntiviral drugDocking (animal)VirusViral replicationRNA-dependent RNA polymeraseComputational biologyPolymeraseEnzymeBiologyBiochemistryMethylation

Abstract

fetched live from OpenAlex

Zika virus (ZIKV) is an emerging mosquito-borne virus recently linked to intrauterine growth restriction including abnormal fetal brain development. The recent outbreak of ZIKV reached pandemic level resulting in an alarming public health emergency. At present, there is limited understanding of the infectious mechanism and no approved therapy. Nonstructural protein 5 is essential for capping and replication of viral RNA and comprises a methyltransferase (MTase) and RNA dependent RNA polymerase domain. Here we used molecular modeling to obtain the structure of ZIKV MTase and molecular docking to identify the additional hydrophobic region uniquely conserved in flavivirus MTase that can be used as a druggable site. Subsequently, a virtual screening with a library of 28 341 compounds identified 10 best hits showing decisive contacts with the MTase. In vitro efficacy analysis of these compounds against ZIKV, by plaque reduction assay, has confirmed four of the top scored ligands (Life Chemicals ID: F3043-0013, F0922-0796, F1609-0442, and F1750-0048) having EC50 (50% effective concentration) values of 4.8 ± 2.3, 12.5 ± 7.4, 17.5 ± 8.4, and 17.6 ± 3.1 μM respectively, identifying lead compounds for anti-ZIKV drug development.

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 score0.010
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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

Citations55
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicMosquito-borne diseases and controlFrench-language works237,207