Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the data. Furthermore, these techniques struggle to represent the conformational heterogeneity averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a Bayesian inference approach to determine single-structure models as well as structural ensembles from cryo-EM maps. EMMIVox automatically balances experimental information with accurate physico-chemical models of the system and the surrounding environment, including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as inference of accurate B-factors enable determination of structural models and ensembles with both excellent fit to the data and high stereochemical quality, thus outperforming state-of-the-art refinement techniques. EMMIVox represents a flexible approach to determine high-quality structural models that will contribute to advancing our understanding of the molecular mechanisms underlying biological functions. EMMIVox introduces an innovative Bayesian inference method designed to generate precise structural models from cryo-electron microscopy data. Unlike many existing techniques that often compromise structural quality to better fit the data, EMMIVox harmoniously balances experimental data with accurate physico-chemical models. This unique approach also allows EMMIVox to describe structural heterogeneity that is frequently overlooked by conventional methods, providing researchers with both single-structure models and structural ensembles. By explicitly addressing challenges such as data correlation and noise, and by incorporating precise B-factor determination, EMMIVox achieves remarkable improvements in data fitting and stereochemical quality compared to current refinement techniques. Its flexibility and accuracy not only enhance the quality of cryo-EM structural models but also promise to significantly advance our understanding of the complex molecular mechanisms underlying biological functions. In doing so, EMMIVox effectively bridges the gap between complex experimental data and insightful structural models.