A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variable
Julian Giraldo-Barreto, Sebastian Ortiz, Erik H. Thiede, Karen, Palacio-Rodriguez, Bob Carpenter, Alex H. Barnett, Pilar Cossio

TL;DR
This paper introduces cryo-BIFE, a Bayesian method to extract free energy profiles from cryo-EM data, enabling insights into biomolecular conformational landscapes despite low SNR.
Contribution
The work presents a novel Bayesian framework using a path collective variable to recover free energy profiles from cryo-EM images, validated on synthetic and real data.
Findings
Successfully recovered free energy landscapes in synthetic systems.
Identified metastable states and activation barriers in a calcium channel.
Demonstrated the method's effectiveness on real cryo-EM particles.
Abstract
Cryo-electron microscopy (cryo-EM) extracts single-particle density projections of individual biomolecules. Although cryo-EM is widely used for 3D reconstruction, due to its single-particle nature, it has the potential to provide information about the biomolecule's conformational variability and underlying free energy landscape. However, treating cryo-EM as a single-molecule technique is challenging because of the low signal-to-noise ratio (SNR) in the individual particles. In this work, we developed the cryo-BIFE method, cryo-EM Bayesian Inference of Free Energy profiles, that uses a path collective variable to extract free energy profiles and their uncertainties from cryo-EM images. We tested the framework over several synthetic systems, where we controlled the imaging parameters and conditions. We found that for realistic cryo-EM environments and relevant biomolecular systems, it is…
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Taxonomy
TopicsAdvanced Electron Microscopy Techniques and Applications · Electron and X-Ray Spectroscopy Techniques · Surface and Thin Film Phenomena
