Evidence map›Paper›PMID 38784472›Full record

ReviewRSC medicinal chemistry2024

Inhibition of bacterial RNA polymerase function and protein-protein interactions: a promising approach for next-generation antibacterial therapeutics.

Jiqing Ye, Cheuk Hei Kan, Xiao Yang, Cong Ma

Abstract readReview
In one paragraph

Review in RSC medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiqing YeState Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Kowloon Hong Kong SAR China cong.ma@polyu.edu.hk.ORCID https://orcid.org/0000-0003-2461-4325
Cheuk Hei KanDepartment of Microbiology, The Chinese University of Hong Kong, Prince of Wales Hospital Shatin Hong Kong SAR China xiaoyang@cuhk.edu.hk.ORCID https://orcid.org/0000-0003-1246-4809
Xiao YangDepartment of Microbiology, The Chinese University of Hong Kong, Prince of Wales Hospital Shatin Hong Kong SAR China xiaoyang@cuhk.edu.hk.
Cong MaState Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Kowloon Hong Kong SAR China cong.ma@polyu.edu.hk.ORCID https://orcid.org/0000-0001-9245-0356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing prevalence of multidrug-resistant pathogens necessitates the urgent development of new antimicrobial agents with innovative modes of action for the next generation of antimicrobial therapy. Bacterial transcription has been identified and widely studied as a viable target for antimicrobial development. The main focus of these studies has been the discovery of inhibitors that bind directly to the core enzyme of RNA polymerase (RNAP). Over the past two decades, substantial advancements have been made in understanding the properties of protein-protein interactions (PPIs) and gaining structural insights into bacterial RNAP and its associated factors. This has led to the crucial role of computational methods in aiding the identification of new PPI inhibitors to affect the RNAP function. In this context, bacterial transcriptional PPIs present promising, albeit challenging, targets for the creation of new antimicrobials. This review will succinctly outline the structural foundation of bacterial transcription networks and provide a summary of the known small molecules that target transcription PPIs.

Identifiers

PMID38784472
PMCPMC11110800

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.