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Record W2277844282 · doi:10.1021/acs.jmedchem.5b01311

Design and Structural Characterization of Potent and Selective Inhibitors of Phosphatidylinositol 4 Kinase IIIβ

2016· article· en· W2277844282 on OpenAlexafffund
Florentine U. Rutaganira, M.L. Fowler, Jacob A. McPhail, Michael A. Gelman, Khanh Cong Nguyen, Anming Xiong, Gillian L. Dornan, Brandon Tavshanjian, Jeffrey S. Glenn, Kevan M. Shokat, John E. Burke

Bibliographic record

VenueJournal of Medicinal Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsUniversity of Victoria
FundersInstitute of Infection and ImmunityNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNIH Office of the DirectorNational Institutes of HealthHoward Hughes Medical Institute
KeywordsChemistryPhosphatidylinositolEnzymeKinaseStructure–activity relationshipBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Type III phosphatidylinositol 4-kinase (PI4KIIIβ) is an essential enzyme in mediating membrane trafficking and is implicated in a variety of pathogenic processes. It is a key host factor mediating replication of RNA viruses. The design of potent and specific inhibitors of this enzyme will be essential to define its cellular roles and may lead to novel antiviral therapeutics. We previously reported the PI4K inhibitor PIK93, and this compound has defined key functions of PI4KIIIβ. However, this compound showed high cross reactivity with class I and III PI3Ks. Using structure-based drug design, we have designed novel potent and selective (>1000-fold over class I and class III PI3Ks) PI4KIIIβ inhibitors. These compounds showed antiviral activity against hepatitis C virus. The co-crystal structure of PI4KIIIβ bound to one of the most potent compounds reveals the molecular basis of specificity. This work will be vital in the design of novel PI4KIIIβ inhibitors, which may play significant roles as antiviral therapeutics.

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 score0.008
Threshold uncertainty score0.259

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations66
Published2016
Admission routes2
Has abstractyes

Explore more

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