cryoWEight: Conformational ensembles from cryo-EM particle images with weighted ensemble simulations
- 1Flatiron Institute
Abstract
Atomistic simulations often struggle to capture the full conformational landscape of biomolecular complexes on experimentally relevant timescales, leaving gaps in our understanding of functionally relevant thermodynamics and kinetics. Cryo-electron microscopy (cryo-EM) provides high-resolution structural snapshots spanning multiple conformational states, but translating these static images into dynamic mechanisms remains challenging. Existing approaches for integrating cryo-EM data with molecular simulations either perturb the underlying dynamics through artificial biasing potentials or remain confined to conformations already present in the prior ensemble. We introduce cryoWEight, an automated and extensible Python framework that couples iterative weighted ensemble (WE) simulations with ensemble reweighting against cryo-EM particle images to steer simulation trajectories towards conformational states consistent with cryo-EM populations. We have found that reweighting in the image space introduces a directional bias towards the target distribution even when the prior ensemble has no overlap with the target distribution in the collective variable space. The reweighting requires only the state likelihoods that set the ensemble weights, and any observable that defines these likelihoods can serve as the target distribution, therefore generalizing beyond the application of cryo-EM. cryoWEight is validated on three proteins: chignolin, the N-terminal domain of ribosomal protein L9, and adenylate kinase. The framework converges towards the target conformational states across different sampling strategies and noise levels, providing a scalable route to integrate cryo-EM data with enhanced sampling approaches.
Related
- Autofocusing molecular simulations for integrative structural biology of broad conformational ensembles
- mdRNAcat: A Molecular Dynamics RNA Catalog of REST2 Conformational Ensembles
- DL3D: visual representation of conformational ensembles of domain-linker-domain proteins
- Chameleonicity Beyond Compactness: Conformational Persistence and Passive Permeability Across 20 PROTAC Ensembles
- Electron Paramagnetic Resonance (EPR) Spectroscopy Reveals Nucleotide- and Partner-Dependent Conformational Ensembles in Hsp90.
BibTeX
@misc{ojha2026cryoweight,
title = {{cryoWEight: Conformational ensembles from cryo-EM particle images with weighted ensemble simulations}},
author = {A. A. Ojha and P. R. Prabhakar and I. Andricioaei and S. M. Hanson},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.28.755042},
url = {https://www.biorxiv.org/content/10.64898/2026.09.28.755042v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - cryoWEight: Conformational ensembles from cryo-EM particle images with weighted ensemble simulations
AU - A. A. Ojha
AU - P. R. Prabhakar
AU - I. Andricioaei
AU - S. M. Hanson
PY - 2026
DA - 2026/09/28
PB - bioRxiv
DO - 10.64898/2026.09.28.755042
UR - https://www.biorxiv.org/content/10.64898/2026.09.28.755042v1
AB - Atomistic simulations often struggle to capture the full conformational landscape of biomolecular complexes on experimentally relevant timescales, leaving gaps in our understanding of functionally relevant thermodynamics and kinetics. Cryo-electron microscopy (cryo-EM) provides high-resolution structural snapshots spanning multiple conformational states, but translating these static images into dynamic mechanisms remains challenging. Existing approaches for integrating cryo-EM data with molecular simulations either perturb the underlying dynamics through artificial biasing potentials or remain confined to conformations already present in the prior ensemble. We introduce cryoWEight, an automated and extensible Python framework that couples iterative weighted ensemble (WE) simulations with ensemble reweighting against cryo-EM particle images to steer simulation trajectories towards conformational states consistent with cryo-EM populations. We have found that reweighting in the image space introduces a directional bias towards the target distribution even when the prior ensemble has no overlap with the target distribution in the collective variable space. The reweighting requires only the state likelihoods that set the ensemble weights, and any observable that defines these likelihoods can serve as the target distribution, therefore generalizing beyond the application of cryo-EM. cryoWEight is validated on three proteins: chignolin, the N-terminal domain of ribosomal protein L9, and adenylate kinase. The framework converges towards the target conformational states across different sampling strategies and noise levels, providing a scalable route to integrate cryo-EM data with enhanced sampling approaches.
ER -