Multi-Modal Ensemble Refinement: Merging Cryo-EM, NMR, and MD for Unified Biomolecular Dynamics Models

Building on the integrative ideas in Son et al. (2024) and the ensemble modeling advances in Wankowicz et al. (2024), this direction aims to synthesize the complementary strengths of cryo-EM (structural snapshots), NMR (dynamic information), and MD simulations (atomistic trajectories). While previous work has combined pairs of these techniques, a fully unified refinement pipeline—where all three data types constrain the same ensemble model—remains unexplored. The novelty here is in developing new algorithms and statistical frameworks to reconcile differences in spatial, temporal, and population scales across these modalities, resulting in physically plausible, experimentally validated models of biomolecular dynamics. This could greatly enhance the accuracy of dynamic models and provide new insights into functionally relevant motions that escape any single method.

References:

1. Automated multiconformer model building for X-ray crystallography and cryo-EM. Stephanie A. Wankowicz, Ashraya Ravikumar, Shivani Sharma, Blake T. Riley, Akshay Raju, Daniel W Hogan, Jessica Flowers, Henry van den Bedem, D. Keedy, J. Fraser (2024). eLife.
2. Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics. Ahrum Son, Woojin Kim, Jongham Park, Wonseok Lee, Yerim Lee, Seongyun Choi, Hyunsoo Kim (2024). International Journal of Molecular Sciences.