Related Experiment Video
Updated: May 28, 2026
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Integrated framework for targeting dynamic penicillin-binding protein 2a via ensemble structural biology and deep
Tianshu Pang1, Xiangyu Zhang1, Yuan Zhou1
1Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine,, Hangzhou, China.
Abstract:
Methicillin-resistant Staphylococcus aureus (MRSA) remains a major cause of morbidity and mortality worldwide, largely due to penicillin-binding protein 2a (PBP2A), a low-affinity transpeptidase encoded by mecA. To address the challenge of this highly flexible target, we developed an integrated discovery framework combining conformational ensemble analysis with deep generative modeling. Extensive molecular dynamics simulations were used to map the dynamic landscape of PBP2A and identify cryptic binding pockets. A target-conditioned generative workflow then explored these states to produce novel candidate scaffolds, followed by multi-objective optimization and structural prioritization. Among the generated molecules, Compound 1 showed a conserved binding mode, sustained stability over 1000 ns MD simulations, and more favorable MMGBSA binding energies than native ligands. Whole-cell assays showed dose-dependent growth inhibition against S. aureus ATCC 29213 and MRSA ATCC 43300, with stronger activity after 24 h exposure. Additional propidium iodide staining revealed a concentration-dependent increase in PI uptake in both strains, consistent with compound-induced envelope perturbation. These findings support Compound 1 as an antibacterial lead emerging from a PBP2A-guided discovery strategy, while direct target engagement remains to be established.
Insights
A novel computational framework identified Compound 1, a promising new antibacterial lead targeting Methicillin-resistant Staphylococcus aureus (MRSA). This compound inhibits bacterial growth and perturbs the cell envelope, offering a potential new strategy against this resistant pathogen.
Area of Science:
- Computational chemistry
- Drug discovery
- Microbiology
Background:
- Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant global health threat.
- The mecA gene, encoding penicillin-binding protein 2a (PBP2A), confers resistance.
- PBP2A's flexibility presents a challenge for drug development.
Purpose of the Study:
- To develop an integrated discovery framework for identifying novel MRSA inhibitors.
- To explore the dynamic landscape of PBP2A and identify cryptic binding pockets.
- To generate and prioritize novel candidate scaffolds targeting PBP2A.
Main Methods:
- Conformational ensemble analysis and deep generative modeling.
- Molecular dynamics (MD) simulations to map PBP2A dynamics.
- Target-conditioned generative workflow for scaffold generation.
- Multi-objective optimization and structural prioritization.
Main Results:
- Compound 1 demonstrated a conserved binding mode and stability in MD simulations.
- Compound 1 exhibited favorable binding energies compared to native ligands.
- Whole-cell assays showed dose-dependent growth inhibition of S. aureus and MRSA.
- Propidium iodide staining indicated compound-induced cell envelope perturbation.
Conclusions:
- Compound 1 is a potential antibacterial lead identified through a PBP2A-guided strategy.
- The developed framework effectively generated novel drug candidates.
- Further studies are needed to confirm direct target engagement of Compound 1.
More Related Videos
22:10Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit
Published on: June 28, 2013
08:31Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020