Integrated framework for targeting dynamic penicillin-binding protein 2a via ensemble structural biology and deep...

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.