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Updated: May 21, 2026
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Homology modeling, molecular docking and electrostatic potential analysis of MurF ligase from Klebsiella pneumonia
Abstract:
In spite of availability of moderately protective vaccine and antibiotics, new antibacterial agents are urgently needed to decrease the global incidence of Klebsiella pneumonia infections. MurF ligase, a key enzyme, which participates in the bacterial cell wall assembly, is indispensable to existence of K. pneumonia. MurF ligase lack mammalian vis-à-vis and have high specificity, uniqueness, and occurrence only in eubacteria, epitomizing them as promising therapeutic targets for intervention. In this study, we present a unified approach involving homology modeling and molecular docking studies on MurF ligase enzyme. As part of this study, a homology model of K. pneumonia (MurF ligase) enzyme was predicted for the first time in order to carry out structurebased drug design. The accuracy of the model was further validated using different computational approaches. The comparative molecular docking study on this enzyme was undertaken using different phyto-ligands from Desmodium sp. and a known antibiotic Ciprofloxacin. The docking analysis indicated the importance of hotspots (HIS 281 and ASN 282) within the MurF binding pocket. The Lipinski's rule of five was analyzed for all ligands considered for this study by calculating the ADME/Tox, drug likeliness using Qikprop simulation. Only ten ligands were found to comply with the Lipinski rule of five. Based on the molecular docking results and Lipinki values 6-Methyltetrapterol A was confirmed as a promising lead compound. The present study should therefore play a guiding role in the experimental design and development of 6-Methyltetrapterol A as a bactericidal agent.
Insights
New antibacterial agents are needed to combat Klebsiella pneumoniae infections. This study identified 6-Methyltetrapterol A as a promising lead compound by modeling and docking the essential bacterial enzyme MurF ligase.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Discovery
Background:
- Klebsiella pneumoniae infections pose a global health threat, necessitating novel antibacterial agents.
- MurF ligase, crucial for bacterial cell wall synthesis, is a validated target due to its absence in mammals and specificity for eubacteria.
Purpose of the Study:
- To develop a homology model of K. pneumoniae MurF ligase for structure-based drug design.
- To identify potential therapeutic compounds targeting MurF ligase using molecular docking and computational analysis.
Main Methods:
- Homology modeling was used to predict the 3D structure of K. pneumoniae MurF ligase.
- Molecular docking simulations were performed with various phytoligands and ciprofloxacin against the MurF model.
- ADME/Tox and drug likeness were assessed using Lipinski's rule of five and Qikprop simulations.
Main Results:
- A validated homology model of K. pneumoniae MurF ligase was generated.
- Docking analysis revealed key binding hotspots (HIS 281, ASN 282) in the MurF active site.
- Ten phytoligands satisfied Lipinski's rule of five, with 6-Methyltetrapterol A identified as a promising candidate.
Conclusions:
- 6-Methyltetrapterol A demonstrates potential as a bactericidal agent against K. pneumoniae.
- This study provides a foundation for experimental development of 6-Methyltetrapterol A.
- Computational approaches are valuable for identifying novel drug leads against bacterial targets.
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