A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments

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 a biomolecule’s conformational variability a...

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Autores principales:	Julian Giraldo-Barreto, Sebastian Ortiz, Erik H. Thiede, Karen Palacio-Rodriguez, Bob Carpenter, Alex H. Barnett, Pilar Cossio
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6d37047f25404889a06af55c9b39dae6
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spelling	oai:doaj.org-article:6d37047f25404889a06af55c9b39dae62021-12-02T16:31:50ZA Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments10.1038/s41598-021-92621-12045-2322https://doaj.org/article/6d37047f25404889a06af55c9b39dae62021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92621-1https://doaj.org/toc/2045-2322Abstract 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 a 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 individual particles. In this work, we propose the cryo-BIFE method (cryo-EM Bayesian Inference of Free-Energy profiles), which uses a path collective variable to extract free-energy profiles and their uncertainties from cryo-EM images. We test the framework on several synthetic systems where the imaging parameters and conditions were controlled. We found that for realistic cryo-EM environments and relevant biomolecular systems, it is possible to recover the underlying free energy, with the pose accuracy and SNR as crucial determinants. We then use the method to study the conformational transitions of a calcium-activated channel with real cryo-EM particles. Interestingly, we recover not only the most probable conformation (used to generate a high-resolution reconstruction of the calcium-bound state) but also a metastable state that corresponds to the calcium-unbound conformation. As expected for turnover transitions within the same sample, the activation barriers are on the order of $$k_BT$$ k B T . We expect our tool for extracting free-energy profiles from cryo-EM images to enable more complete characterization of the thermodynamic ensemble of biomolecules.Julian Giraldo-BarretoSebastian OrtizErik H. ThiedeKaren Palacio-RodriguezBob CarpenterAlex H. BarnettPilar CossioNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Julian Giraldo-Barreto Sebastian Ortiz Erik H. Thiede Karen Palacio-Rodriguez Bob Carpenter Alex H. Barnett Pilar Cossio A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
description	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 a 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 individual particles. In this work, we propose the cryo-BIFE method (cryo-EM Bayesian Inference of Free-Energy profiles), which uses a path collective variable to extract free-energy profiles and their uncertainties from cryo-EM images. We test the framework on several synthetic systems where the imaging parameters and conditions were controlled. We found that for realistic cryo-EM environments and relevant biomolecular systems, it is possible to recover the underlying free energy, with the pose accuracy and SNR as crucial determinants. We then use the method to study the conformational transitions of a calcium-activated channel with real cryo-EM particles. Interestingly, we recover not only the most probable conformation (used to generate a high-resolution reconstruction of the calcium-bound state) but also a metastable state that corresponds to the calcium-unbound conformation. As expected for turnover transitions within the same sample, the activation barriers are on the order of $$k_BT$$ k B T . We expect our tool for extracting free-energy profiles from cryo-EM images to enable more complete characterization of the thermodynamic ensemble of biomolecules.
format	article
author	Julian Giraldo-Barreto Sebastian Ortiz Erik H. Thiede Karen Palacio-Rodriguez Bob Carpenter Alex H. Barnett Pilar Cossio
author_facet	Julian Giraldo-Barreto Sebastian Ortiz Erik H. Thiede Karen Palacio-Rodriguez Bob Carpenter Alex H. Barnett Pilar Cossio
author_sort	Julian Giraldo-Barreto
title	A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
title_short	A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
title_full	A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
title_fullStr	A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
title_full_unstemmed	A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
title_sort	bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6d37047f25404889a06af55c9b39dae6
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A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments

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