A dataset detailing an integrated discovery framework for novel antibacterial compounds targeting penicillin-binding protein 2a (PBP2a) in methicillin-resistant Staphylococcus aureus (MRSA). The framework combines conformational ensemble analysis from molecular dynamics simulations with deep generative modeling to produce candidate scaffolds. The dataset includes results for a lead compound, Compound 1, showing growth inhibition in whole-cell assays against S. aureus strains.
Use Cases
- Training generative models for scaffold design based on cryptic binding pockets identified in molecular dynamics simulations.
- Analyzing the relationship between MMGBSA binding energies and antibacterial activity for generated compounds.
- Benchmarking multi-objective optimization strategies for prioritizing novel antibacterial leads from generative workflows.
- Studying compound-induced envelope perturbation in bacterial strains via propidium iodide uptake data.
Strengths
- The dataset is derived from extensive molecular dynamics simulations used to map the dynamic landscape of PBP2a.
- Includes validation data from whole-cell assays showing dose-dependent growth inhibition against two S. aureus strains.
- Lead compound stability was assessed over 1000 ns MD simulations, providing a concrete performance metric.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The dataset is small in scale at 155.1 KB, indicating limited scope.
Provenance
- Source
- Tianshu Pang via figshare.
- Collection Method
- Generated via an integrated framework combining conformational ensemble analysis from molecular dynamics with deep generative modeling.
- Freshness
- Last updated 2026-05-11 04:27:21; freshness should be verified.