wepy

wepy simulates rare or long-timescale molecular events using the weighted ensemble (WE) algorithm in Python to enhance sampling of processes such as protein folding and unfolding, ligand binding and unbinding, and large conformational changes.

Key Features:

• Modular architecture: Provides extensible, modular components enabling custom progress variables, analysis functions, parallel simulation, and weighted ensemble resampling.
• Python implementation and integrations: Implemented in pure Python with integrations for OpenMM and use of mdtraj for trajectory analysis, scikit-learn for machine learning tasks, numpy for numerical computations, and scipy for scientific computing.
• Adaptive resampling algorithms: Supports high-dimensional adaptive resampling algorithms including WExplore and REVO to concentrate sampling on important regions.
• Algorithm development framework: Offers a structured framework for developing and testing new resampling algorithms.

Scientific Applications:

• Protein folding and unfolding: Enables enhanced sampling and kinetic characterization of folding and unfolding pathways.
• Ligand binding and unbinding: Facilitates simulation of ligand association and dissociation events and their pathways.
• Conformational changes and structural rearrangements: Samples large conformational transitions and structural rearrangements in biomolecules.
• Resampling algorithm research: Provides a platform for developing and evaluating new weighted ensemble and adaptive resampling methodologies.

Methodology:

Uses the weighted ensemble (WE) algorithm by partitioning simulation space into multiple ensembles or pathways with periodic importance-based resampling; supports high-dimensional adaptive resampling methods (WExplore, REVO) and interfaces with OpenMM, mdtraj, scikit-learn, numpy, and scipy.