ArticleIUCrJ2021
Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination.
Article in IUCrJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Confidence-guided cryo-EM map optimisation with LocScale-2.0.Nature communications · 2026Article
- Efficient high-resolution refinement in cryo-EM with stochastic gradient descent.Acta crystallographica. Section D, Structural biology · 2025Article
- Article
- InstaMap: instant-NGP for cryo-EM density maps.Acta crystallographica. Section D, Structural biology · 2025Article
- Data-driven regularization lowers the size barrier of cryo-EM structure determination.Nature methods · 2024Article
- Improvement of cryo-EM maps by simultaneous local and non-local deep learning.Nature communications · 2023Article
- Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.Current opinion in structural biology · 2023Review
- Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit.SIAM journal on imaging sciences · 2023Article
- Article
- A molecular prior distribution for Bayesian inference based on Wilson statistics.Computer methods and programs in biomedicine · 2022Article
- Real space in cryo-EM: the future is local.Acta crystallographica. Section D, Structural biology · 2022Article
- New tools for automated cryo-EM single-particle analysis in RELION-4.0.The Biochemical journal · 2021Article
- Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.Annual review of biophysics · 2021Review
- Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective.Frontiers in molecular biosciences · 2021Article
- Density modification of cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2020Article
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Authors and funding
8 authors.
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Abstract
Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed.
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