Molecular dynamics (MD) simulations are a powerful tool for understanding molecular behavior, but simulating rare events—such as protein folding, ligand binding, or conformational transitions—remains computationally challenging due to long waiting times and high free-energy barriers. Recent advances in enhanced sampling methods have addressed these challenges by intelligently guiding simulations toward target states and efficiently estimating kinetic properties.

WeTICA: A Directed Search Weighted Ensemble Based Enhanced Sampling Method

WeTICA is a binless Weighted Ensemble (WE) algorithm designed to efficiently estimate rare event kinetics without the need for complex binning schemes.

Key Features

• » Uses low-dimensional Time-lagged Independent Component Analysis (TICA) projections as collective variables (CVs).
• » Enables the direct calculation of kinetic rates from biased trajectories.
• » Applied to unfolding of TC5b and TC10b Trp-cage mutants, as well as Protein G, with high accuracy.
• » Achieves convergence using an order of magnitude less simulation time than conventional WE approaches.
• » Can be extended to other linear or nonlinear CV spaces with improved walker selection criteria.

References

• Mitra et al., J. Chem. Phys. 162, 034106 (2025)

CoWERA: Coherence-based Weighted Ensemble Resampling Algorithm

CoWERA introduces temporal coherence-based resampling to prioritize trajectories that consistently make forward progress toward the target state.

Key Features

• » Focuses computational effort on physically meaningful and productive pathways.
• » Reduces wasteful sampling in unproductive CV regions.
• » Provides faster convergence, lower variance, and more reliable kinetic estimates.
• » Avoids misleading guidance from absolute CV positions, ensuring kinetic relevance.

Applications

Protein folding, ligand unbinding, conformational switching, and other rare-event molecular processes where efficiency and kinetic accuracy are critical.

References

• Work in progress!

PathGennie: Rapid Generation of Rare Event Pathways

PathGennie uses a direction-guided adaptive sampling strategy with ultrashort monitored trajectories to quickly identify rare event pathways.

Key Features

• » Selectively propagates trajectories showing progress toward the target in a predefined CV space.
• » Handles ligand unbinding in T4 Lysozyme and Abl kinase, as well as protein folding (Trp-cage, Protein G).
• » Generates multiple competing pathways and estimates their relative populations.
• » Produces viable transition pathways in a few hundred picoseconds, suitable for further analysis using WE or TPS simulations.

References

• Work in progress!

Open-source code: PathGennie on GitHub