PELE-MSM

PELE-MSM estimates absolute protein–ligand binding free energies by combining the PELE (Protein Energy Landscape Exploration) Monte Carlo sampling method with Markov State Models to characterize conformational landscapes and compute binding free energies.

Key Features:

• PELE Monte Carlo sampling: Employs the PELE (Protein Energy Landscape Exploration) Monte Carlo algorithm to traverse protein energy landscapes and identify binding-relevant conformations.
• Ligand pathway approach: Initiates with a short enhanced sampling simulation to identify viable ligand starting positions for subsequent sampling.
• Markov State Model (MSM) analysis: Uses Markov State Models to represent conformational dynamics as states and transitions, enabling faster convergence of free energy estimates.
• Automated workflow: Implements an autonomous workflow for setup, sampling, and analysis to produce binding free energy estimates.
• Application on diverse systems: Has been applied to four distinct protein–ligand systems and was able to rank compounds effectively in two of those systems.

Scientific Applications:

• Computational structural biology: Characterizes protein–ligand conformational landscapes to inform mechanistic understanding of binding processes.
• Drug discovery and compound ranking: Estimates absolute binding free energies and ranks compounds to support prioritization for experimental validation.

Methodology:

Performs Monte Carlo sampling via the PELE (Protein Energy Landscape Exploration) method, uses a short enhanced sampling simulation to identify ligand starting positions, and applies Markov State Models to model conformational states and compute binding free energies.