CryoPhold: CryoEM meets AlphaFold and molecular simulation to reveal protein dynamics

Abstract Here we are introducing CryoPhold, a modular workflow that unifies AlphaFold-based ensemble generation, Bayesian reweighting against experimental cryo-EM maps, molecular simulation, and machine learning to quantify conformational populations and identify structural fingerprints that govern protein functions. Proteins are inherently dynamic, interconverting among conformational states that govern their function. Perturbations such as mutations, ligand binding, and pH changes modulate these dynamics and are implicated in many diseases. While cryogenic electron microscopy (cryo-EM) has transformed structure determination, it typically yields an averaged density map representing a static snapshot. A central challenge remains capturing the thermodynamics underlying protein motions and corresponding structural fingerprints that modulate function. CryoPhold enables Bayesian reweighting of AlphaFold-generated structural ensembles against experimental cryo-EM maps, generating posterior structural ensembles that are consistent with experimental data while preserving conformational heterogeneity. Molecular simulations seeded from the posterior ensemble capture time-dependent dynamics, while machine learning models trained on featurized molecular simulation data identify structural fingerprints (“hotspots”) that modulate protein dynamics. Finally, Markov state models trained on featurized molecular simulation data quantify metastable state populations and free-energy landscapes. By integrating a generative AI-based protein structure prediction model, experimental cryo-EM density, physics-based sampling, and machine learning, CryoPhold enables dynamics paradigm to capture biomolecular motion. The workflow enables prediction of equilibrium populations and structural fingerprints governing conformational dynamics in human transporter protein, GlyT1. It further captures structural changes and population shifts associated with oncogenic BRAF mutants, key driving factors behind melanoma progression. Competing Interest Statement The authors have declared no competing interest.