A combined in silico and MD simulation approach to discover novel LpxC inhibitors targeting multiple drug resistant Pseudomonas aeruginosa
Pseudomonas aeruginosa (P. aeruginosa), a member of the ESKAPE family, is the major cause of infections leading to increased morbidity and mortality due to multidrug resistance (MDR). One of the main proteins involved in the Raetz pathway is LpxC, which plays a significant role in anti-microbial resistance (AMR). Our study aimed to identify a novel compound to combat MDR due to the LpxC protein. It involved in silico methods comprising molecular docking, simulations, ADMET profiling, and DFT calculations. First, an ADMET and bioactivity evaluation of the 25 top-hit compounds retrieved from ligand-based virtual screening was performed, followed by molecular docking. The results revealed compound P-2 as the lead compound, which was further subjected to DFT analysis and molecular dynamics (MD) simulations. With these analyses, our in silico study identified P-2, 3-[(dimethylamino)methyl]-N-[(2 S)-1-(hydroxyamino)-1-oxobutan-2-yl]benzamide as a potential lead compound that may behave as a very potent inhibitor of LpxC for the development of targeted therapies against MDR P. aeruginosa.
Authors
- Awadh Alanazi (ORCID: https://orcid.org/0000-0002-6750-4815)
- Hammad Saleem (ORCID: https://orcid.org/0000-0002-2541-0271)
- Bi Bi Zainab Mazhari (ORCID: https://orcid.org/0000-0003-1832-4348)
- Khalid Omer Abdalla Abosalif (ORCID: https://orcid.org/0000-0003-3207-7216)
- Hasan Ejaz (ORCID: https://orcid.org/0000-0002-6185-2042)
- Sonia Younas (ORCID: https://orcid.org/0000-0003-4958-8629)
- Abualgasim Elgaili Abdalla (ORCID: https://orcid.org/0000-0002-8680-8427)
- Muharib Alruwaili (ORCID: https://orcid.org/0009-0007-7858-8288)
- Muhammad Umer Khan (ORCID: https://orcid.org/0000-0001-6289-5207)
Institutions
- University of Lahore (PK)
- Jouf University (SA)
- Hong Kong Science and Technology Parks Corporation (HK)
- HKU-Pasteur Research Pole (HK)
- University of Hong Kong (HK)
Publication Details
- Journal
- Scientific Reports
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1038/s41598-025-99215-1
- Citations
- 4
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 6.02