ArticleProceedings of the National Academy of Sciences of the United States of America2021
Sparseness and Smoothness Regularized Imaging for improving the resolution of Cryo-EM single-particle reconstruction.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Cryo-EM Heterogeneity Analysis using Regularized Covariance Estimation and Kernel Regression.bioRxiv : the preprint server for biology · 2024Article
- Familial Alzheimer mutations stabilize synaptotoxic γ-secretase-substrate complexes.Cell reports · 2024Article
- OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis.Nature methods · 2023Article
- Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling.Bioinformatics (Oxford, England) · 2023Article
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5 authors.
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Abstract
In this paper, we present a refinement method for cryo-electron microscopy (cryo-EM) single-particle reconstruction, termed as OPUS-SSRI (Sparseness and Smoothness Regularized Imaging). In OPUS-SSRI, spatially varying sparseness and smoothness priors are incorporated to improve the regularity of electron density map, and a type of real space penalty function is designed. Moreover, we define the back-projection step as a local kernel regression and propose a first-order method to solve the resulting optimization problem. On the seven cryo-EM datasets that we tested, the average improvement in resolution by OPUS-SSRI over that from RELION 3.0, the commonly used image-processing software for single-particle cryo-EM, was 0.64 Å, with the largest improvement being 1.25 Å. We expect OPUS-SSRI to be an invaluable tool to the broad field of cryo-EM single-particle analysis. The implementation of OPUS-SSRI can be found at https://github.com/alncat/cryoem.
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