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10.1109-TCI.2022.3174801 |
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220630s2022 CNT 000 0 und d |
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|a 25730436 (ISSN)
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|a Recovery of Conformational Continuum from Single-particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth
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|b Institute of Electrical and Electronics Engineers Inc.
|c 2022
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|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
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|a Bioinformatics
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|a Biology
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|a biomolecules
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|a Conformations
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|a Degrees of freedom (mechanics)
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|a Electric fields
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|a Embeddings
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|a Free energy
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|a Free energy landscape
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|a free-energy landscape
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|a Image reconstruction
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|a Image reconstruction
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|a Images reconstruction
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|a Imaging
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|a kernel methods
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|a Kernel-methods
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|a Learning systems
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|a Manifold
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|a manifold embedding
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|a Manifold embedding
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|a Manifolds
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|a Medical imaging
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|a Principal component analysis
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|a Principal-component analysis
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|a Proteins
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|a Proteins
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|a quantitative biology
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|a Quantitative biology
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|a Single particle cryogenic microscopy
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|a single particle cryogenic microscopy (cryo-EM)
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|a Single-particle
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|a spectral geometry
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|a Spectral geometry
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|a Three dimensional displays
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|a Three-dimensional display
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|a Three-dimensional displays
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|a unsupervised machine learning
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|a Unsupervised machine learning
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|a Acosta-Reyes, F.
|e author
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|a Frank, J.
|e author
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|a Maji, S.
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|a Schwander, P.
|e author
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|a Seitz, E.
|e author
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|t IEEE Transactions on Computational Imaging
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|z View Fulltext in Publisher
|u https://doi.org/10.1109/TCI.2022.3174801
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