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Record W4220685594 · doi:10.1021/acs.jmedchem.1c02122

Potent Bactericidal Antimycobacterials Targeting the Chaperone ClpC1 Based on the Depsipeptide Natural Products Ecumicin and Ohmyungsamycin A

2022· article· en· W4220685594 on OpenAlexaff
Paige M. E. Hawkins, David M. Hoi, Chen‐Yi Cheung, Trixie Wang, Diana H. Quan, Vishnu Mini Sasi, Dennis Y. Liu, Roger G. Linington, Colin J. Jackson, Stefan H. Oehlers, Gregory M. Cook, Warwick J. Britton, Tim Clausen, Richard J. Payne

Bibliographic record

VenueJournal of Medicinal Chemistry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsSimon Fraser University
FundersHealth Research Council of New ZealandAustralian Research Council
KeywordsDepsipeptideChemistryMycobacterium tuberculosisNatural productIn vitroIn vivoAntimycobacterialBiological activityMicrobiologyProteaseTuberculosisBiochemistryEnzymeBiology

Abstract

fetched live from OpenAlex

(Mtb), the causative agent of tuberculosis (TB). Herein, we describe the design and synthesis of a library of analogues of these two natural products using an efficient solid-phase synthesis and late-stage macrolactamization strategy. Lead analogues possessed potent activity against Mtb in vitro (minimum inhibitory concentration 125-500 nM) and were shown to inhibit protein degradation by the mycobacterial ClpC1-ClpP1P2 protease with an associated enhancement of ClpC1 ATPase activity. The most promising analogue from the series exhibited rapid bactericidal killing activity against Mtb, capable of sterilizing cultures after 7 days, and retained bactericidal activity against hypoxic non-replicating Mtb. This natural product analogue was also active in an in vivo zebrafish model of infection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.208
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2022
Admission routes1
Has abstractyes

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