Discovery of novel DdlA inhibitors in multidrug-resistant Pseudomonas aeruginosa using virtual screening, molecular docking, and dynamics simulations
Abstract Pseudomonas aeruginosa is a gram-negative, opportunistic pathogen that represents a serious risk factor in healthcare services due to its natural resistance mechanisms and the increasing prevalence of multi-drug resistant strains. This study utilized in silico computational approaches to id...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97698-6 |
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| Summary: | Abstract Pseudomonas aeruginosa is a gram-negative, opportunistic pathogen that represents a serious risk factor in healthcare services due to its natural resistance mechanisms and the increasing prevalence of multi-drug resistant strains. This study utilized in silico computational approaches to identify the novel inhibitors for d-alanine–d-alanine ligase A (DdlA), an essential enzyme for the bacterial peptidoglycan biosynthesis pathway necessary for cell wall integrity. A structure-based virtual screening of The Medicinal Fungi Secondary Metabolites and Therapeutics (MeFSAT) chemical library was conducted, followed by molecular docking to evaluate the binding affinity of small molecules to the DdlA active site. MSID000191, MSID000200, and MSID000102 were recognized as the leading candidates in the preliminary docking data due to their low binding energy values. These compounds exhibited binding energies markedly superior to the control drug (d-cycloserine), suggesting a substantial potential for inhibiting the DdlA enzyme. Detailed interaction analyses revealed significant salt bridges and hydrogen bonds with active site residues, which enhance the stability of the complex. Density Functional Theory (DFT) analysis and MMPBSA calculations also provided insights into electronic properties and binding free energy, respectively. These findings highlight the potential of these inhibitors as therapeutic candidates and showcase the effectiveness of computational methods in accelerating drug discovery against multidrug-resistant P. aeruginosa. Future research should incorporate more in-silico techniques and experimental validations to confirm these results. |
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| ISSN: | 2045-2322 |