Recovery of Conformational Continuum from Single-particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth

This work is based on the manifold-embedding approach to study biological molecules exhibiting continuous conformational changes. Previous work established a methodnow termed ManifoldEMcapable of reconstructing 3D movies and accompanying free-energy landscapes from single-particle cryo-EM images of...

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Bibliographic Details
Main Authors: Acosta-Reyes, F. (Author), Frank, J. (Author), Maji, S. (Author), Schwander, P. (Author), Seitz, E. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 03116nam a2200637Ia 4500
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008 220630s2022 CNT 000 0 und d
020 |a 25730436 (ISSN) 
245 1 0 |a Recovery of Conformational Continuum from Single-particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth 
260 0 |b Institute of Electrical and Electronics Engineers Inc.  |c 2022 
520 3 |a This work is based on the manifold-embedding approach to study biological molecules exhibiting continuous conformational changes. Previous work established a methodnow termed ManifoldEMcapable of reconstructing 3D movies and accompanying free-energy landscapes from single-particle cryo-EM images of macromolecules exercising multiple conformational degrees of freedom. While ManifoldEM has proven its viability in several experimental studies, critical limitations and uncertainties have been found throughout its extended development and use. Guided by insights from studies with cryo-EM ground-truth data, simulated from atomic structures undergoing conformational changes, we have built a novel framework, ESPER, able to retrieve the free-energy landscape and respective 3D Coulomb potential maps for all states simulated. As shown by a direct comparison of ground truth vs. recovered maps, and analysis of experimental data from the 80S ribosome and ryanodine receptor, ESPER offers substantial improvements relative to the previous work. Author 
650 0 4 |a Bioinformatics 
650 0 4 |a Biology 
650 0 4 |a biomolecules 
650 0 4 |a Conformations 
650 0 4 |a Degrees of freedom (mechanics) 
650 0 4 |a Electric fields 
650 0 4 |a Embeddings 
650 0 4 |a Free energy 
650 0 4 |a Free energy landscape 
650 0 4 |a free-energy landscape 
650 0 4 |a Image reconstruction 
650 0 4 |a Image reconstruction 
650 0 4 |a Images reconstruction 
650 0 4 |a Imaging 
650 0 4 |a kernel methods 
650 0 4 |a Kernel-methods 
650 0 4 |a Learning systems 
650 0 4 |a Manifold 
650 0 4 |a manifold embedding 
650 0 4 |a Manifold embedding 
650 0 4 |a Manifolds 
650 0 4 |a Medical imaging 
650 0 4 |a Principal component analysis 
650 0 4 |a Principal-component analysis 
650 0 4 |a Proteins 
650 0 4 |a Proteins 
650 0 4 |a quantitative biology 
650 0 4 |a Quantitative biology 
650 0 4 |a Single particle cryogenic microscopy 
650 0 4 |a single particle cryogenic microscopy (cryo-EM) 
650 0 4 |a Single-particle 
650 0 4 |a spectral geometry 
650 0 4 |a Spectral geometry 
650 0 4 |a Three dimensional displays 
650 0 4 |a Three-dimensional display 
650 0 4 |a Three-dimensional displays 
650 0 4 |a unsupervised machine learning 
650 0 4 |a Unsupervised machine learning 
700 1 0 |a Acosta-Reyes, F.  |e author 
700 1 0 |a Frank, J.  |e author 
700 1 0 |a Maji, S.  |e author 
700 1 0 |a Schwander, P.  |e author 
700 1 0 |a Seitz, E.  |e author 
773 |t IEEE Transactions on Computational Imaging 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1109/TCI.2022.3174801