Evidence map›Paper›PMID 39501032›Full record

ArticleScientific reports2024

In silico discovery of druggable targets in Citrobacter koseri using echinoderm metabolites and molecular dynamics simulation.

Bayan A Alhaidhal, Fatimah M Alsulais, Ramzi A Mothana, Abdullah R Alanzi

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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.

Bayan A AlhaidhalDepartment of Pharmacognosy, College of Pharmacy, King Saud University, PO Box 2457, Riyadh, 11451, Saudi Arabia.
Fatimah M AlsulaisDepartment of Pharmacognosy, College of Pharmacy, King Saud University, PO Box 2457, Riyadh, 11451, Saudi Arabia.
Ramzi A MothanaDepartment of Pharmacognosy, College of Pharmacy, King Saud University, PO Box 2457, Riyadh, 11451, Saudi Arabia.
Abdullah R AlanziDepartment of Pharmacognosy, College of Pharmacy, King Saud University, PO Box 2457, Riyadh, 11451, Saudi Arabia. aralonazi@ksu.edu.sa.

Funding

King Saud University Riyadh Saudi Arabia RSPD2024R885
6 · The paper itself

Abstract

Citrobacter koseri causes infection in people who are immunocompromised. Without effective antibiotics, these infections can become severe and life-threatening, so effective drugs are essential to treat these infections. Utilizing subtractive genomics, 2699 ORFs were predicted and translated into amino acid sequences. Metabolic pathway analysis and subcellular localization helped define the roles of key bacterial proteins. Two druggable proteins, WP_012000829.1 and WP_275157394.1, were discovered as promising targets. Alpha Fold provided 3D structures, and a library of 1600 echinoderm metabolites was docked against these proteins, with Ampicillin, Levofloxacin, and Doxycycline as controls. Notably, CMNPD13085 and CMNPD15632 exhibited the highest binding affinities for WP_012000829.1 and WP_275157394.1, respectively. Molecular dynamics simulations and MM-GBSA binding free energy complemented docking results. However, acknowledging the reliance on computational validations, the study emphasizes the need for essential in-vitro research to transform these potential inhibitors into therapeutic drugs.

Indexed as

Anti-Bacterial AgentsCitrobacter koseriMolecular Docking SimulationMolecular Dynamics SimulationAnimalsBacterial ProteinsComputer SimulationDrug DiscoveryHumansAnti-Bacterial AgentsBacterial ProteinsCitrobacter koseriImmunocompromisedMD simulationMMGBSAMolecular dockingSubtractive genomics

Identifiers

PMID39501032
PMCPMC11538563

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.