Bio-evaluation and molecular modeling studies of ARG-TRP-ARG-based peptides as antimicrobial agents

Emmanuel T. Akintayo (), Olaoye Stella (), Sunday Adewale Akintelu () and Abel Kolawole Oyebamiji ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 11, 360-386

Abstract: The battle against bacterial infections in both young and elderly populations continues to be a focus for researchers worldwide. This issue has led to the discovery of the most effective antibacterial ARG-TRP-ARG-based peptides through density functional theory and molecular modeling analysis. In this research, the inhibitory effectiveness of an ARG-TRP-ARG-based peptide was evaluated using in silico methods. The optimization was carried out with density functional theory, while molecular modeling studies were performed using induced-fit docking and molecular dynamics simulations. Compounds 3 and 4 showed promising responses regarding their HOMO energy, with compound 3 displaying a stronger propensity for favorable reactions in terms of energy gap. It was noted that compound 10, which possessed the lowest LUMO energy value, had the highest ability to accept electrons from nearby compounds. Additionally, when testing the compounds against thymidylate kinase from gram-positive bacteria (4HLC) and gyrase B from Thermus thermophilus (1KIJ), compounds 9 and 5 demonstrated the most significant inhibition of the targeted receptors. Molecular dynamics simulations confirmed the effectiveness of the lead compounds as predicted by the docking approach. Furthermore, the binding affinities were predicted from the calculated descriptors using Python (v3.11), and the results were presented accordingly.

Keywords: Affinity; Amino acids; Bacterial; Drugs; Insilico. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/10874/3497 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:11:p:360-386:id:10874

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().