AIMC Topic: Cryoelectron Microscopy
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I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.
Nature protocols
Aug 5, 2022
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-d...
Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.
Nature communications
Jul 13, 2022
Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-inten...
MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms.
Computer methods and programs in biomedicine
Jul 1, 2022
BACKGROUND AND OBJECTIVE: Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells....
Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold.
Science (New York, N.Y.)
Jun 10, 2022
INTRODUCTION The nuclear pore complex (NPC) is the molecular conduit in the nuclear membrane of eukaryotic cells that regulates import and export of biomolecules between the nucleus and the cytosol, with vertebrate NPCs ~110 to 125 MDa in molecular m...
AI-based structure prediction empowers integrative structural analysis of human nuclear pores.
Science (New York, N.Y.)
Jun 10, 2022
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
Jun 7, 2022
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
Feb 3, 2022
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)
Jan 31, 2022
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
Oct 21, 2021
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
Sep 7, 2021
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...
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