Evidence map›Paper›PMID 35956838›Full record

ArticleMolecules (Basel, Switzerland)2022

In Silico Screening of Quorum Sensing Inhibitor Candidates Obtained by Chemical Similarity Search.

Sharath Belenahalli Shekarappa, Hrvoje Rimac, Julian Lee

Open access · goldAbstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 48% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
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

3 authors at 2 institutions in 2 countries.

Sharath Belenahalli ShekarappaDepartment of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Korea.
Hrvoje RimacDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia.ORCID 0000-0001-7232-6489
Julian LeeDepartment of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Korea.ORCID 0000-0002-6088-8406
Soongsil University · KRUniversity of Zagreb · HR

Funding

National Research Foundation of Korea NRF-2020R1A2C1005956
6 · The paper itself

Abstract

Quorum sensing (QS) is a bacterial communication using signal molecules, by which they sense population density of their own species, leading to group behavior such as biofilm formation and virulence. Autoinducer-2 (AI2) is a QS signal molecule universally used by both gram-positive and gram-negative bacteria. Inhibition of QS mediated by AI2 is important for various practical applications, including prevention of gum-disease caused by biofilm formation of oral bacteria. In this research, molecular docking and molecular dynamics (MD) simulations were performed for molecules that are chemically similar to known AI2 inhibitors that might have a potential to be quorum sensing inhibitors. The molecules that form stable complexes with the AI2 receptor protein were found, suggesting that they could be developed as a novel AI2 inhibitors after further in vitro validation. The result suggests that combination of ligand-based drug design and computational methods such as MD simulation, and experimental verification, may lead to development of novel AI inhibitor, with a broad range of practical applications.

Indexed as

Anti-Bacterial AgentsQuorum SensingBacteriaBacterial ProteinsBiofilmsGram-Negative BacteriaGram-Positive BacteriaMolecular Docking SimulationAnti-Bacterial AgentsBacterial ProteinsAI2 inhibitormolecular dockingmolecular dynamicsquorum sensing

Identifiers

PMID35956838
PMCPMC9369968
OpenAlexW4289204539

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.