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Cryoelectron Microscopy

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AI-based structure prediction empowers integrative structural analysis of human nuclear pores.

Science (New York, N.Y.)
INTRODUCTION The eukaryotic nucleus pro-tects the genome and is enclosed by the two membranes of the nuclear envelope. Nuclear pore complexes (NPCs) perforate the nuclear envelope to facilitate nucleocytoplasmic transport. With a molecular weight of ...

Sequence-assignment validation in cryo-EM models with checkMySequence.

Acta crystallographica. Section D, Structural biology
The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models w...

Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography.

IEEE/ACM transactions on computational biology and bioinformatics
Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecul...

Cryo-EM and artificial intelligence visualize endogenous protein community members.

Structure (London, England : 1993)
Cellular function is underlined by megadalton assemblies organizing in proximity, forming communities. Metabolons are protein communities involving metabolic pathways such as protein, fatty acid, and thioesters of coenzyme-A synthesis. Metabolons are...

Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.

Nature methods
Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and rec...

Machine learning-based real-time object locator/evaluator for cryo-EM data collection.

Communications biology
In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Imple...

Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM.

Nature methods
Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual macromolecules sampling different conformational and...

Accurate prediction of protein structures and interactions using a three-track neural network.

Science (New York, N.Y.)
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track ...

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.

Communications biology
Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on g...

Deep Learning-Based Advances in Protein Structure Prediction.

International journal of molecular sciences
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine prot...