ArticleInverse problems2020
Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes.
Article in Inverse problems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Strategies for studying discrete heterogeneity in situ using cryo-electron tomography.Current opinion in structural biology · 2025Review
- CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets.Nature methods · 2025Article
- Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cryo-EM Heterogeneity Analysis using Regularized Covariance Estimation and Kernel Regression.bioRxiv : the preprint server for biology · 2024Article
- Self Fourier shell correlation: properties and application to cryo-ET.Communications biology · 2024Article
- MDSPACE and MDTOMO Software for Extracting Continuous Conformational Landscapes from Datasets of Single Particle Images and Subtomograms Based on Molecular Dynamics Simulations: Latest Developments in ContinuousFlex Software Package.International journal of molecular sciences · 2023Article
- Self Fourier shell correlation: properties and application to cryo-ET.bioRxiv : the preprint server for biology · 2023Article
- A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps.Nature communications · 2023Article
- 3DFlex: determining structure and motion of flexible proteins from cryo-EM.Nature methods · 2023Article
- Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity.Journal of molecular biology · 2023Review
- Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy.Micromachines · 2022Review
- Emerging Themes in CryoEM─Single Particle Analysis Image Processing.Chemical reviews · 2022Review
- Ab-initio contrast estimation and denoising of cryo-EM images.Computer methods and programs in biomedicine · 2022Article
- Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning.International journal of molecular sciences · 2022Article
- Data-driven determination of number of discrete conformations in single-particle cryo-EM.Computer methods and programs in biomedicine · 2022Article
- DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images.Frontiers in molecular biosciences · 2022Article
- Recovery of Conformational Continuum From Single-Particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth.IEEE transactions on computational imaging · 2022Article
- Correlated Motions in Structural Biology.Biochemistry · 2021Review
- Structural Heterogeneities of the Ribosome: New Frontiers and Opportunities for Cryo-EM.Molecules (Basel, Switzerland) · 2020Review
- Computational Methods for Single-Particle Electron Cryomicroscopy.Annual review of biomedical data science · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Single-particle electron cryomicroscopy is an essential tool for high-resolution 3D reconstruction of proteins and other biological macromolecules. An important challenge in cryo-EM is the reconstruction of non-rigid molecules with parts that move and deform. Traditional reconstruction methods fail in these cases, resulting in smeared reconstructions of the moving parts. This poses a major obstacle for structural biologists, who need high-resolution reconstructions of entire macromolecules, moving parts included. To address this challenge, we present a new method for the reconstruction of macromolecules exhibiting continuous heterogeneity. The proposed method uses projection images from multiple viewing directions to construct a graph Laplacian through which the manifold of three-dimensional conformations is analyzed. The 3D molecular structures are then expanded in a basis of Laplacian eigenvectors, using a novel generalized tomographic reconstruction algorithm to compute the expansion coefficients. These coefficients, which we name
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