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Cryoelectron Microscopy

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Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning.

Nature communications
An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there ar...

DeepAlign, a 3D alignment method based on regionalized deep learning for Cryo-EM.

Journal of structural biology
Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens at high resolution. Despite the great development of the techniques involved to solve the biological structures with Cr...

Cryo-Electron Microscopic Grid Preparation for Time-Resolved Studies using a Novel Robotic System, Spotiton.

Journal of visualized experiments : JoVE
The capture of short-lived molecular states triggered by the early encounter of two or more interacting particles continues to be an experimental challenge of great interest to the field of cryo-electron microscopy (cryo-EM). A few methodological str...

CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy.

Communications biology
Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determ...

CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks.

Nature methods
Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major ch...

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.

BMC bioinformatics
BACKGROUND: Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the di...

Topaz-Denoise: general deep denoising models for cryoEM and cryoET.

Nature communications
Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data proces...

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.

Angewandte Chemie (International ed. in English)
In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the const...

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.

Journal of structural biology
Cryo-EM Single Particle Analysis workflows require tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-...

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials.

Journal of chemical information and modeling
Despite significant advances in resolution, the potential for cryo-electron microscopy (EM) to be used in determining the structures of protein-drug complexes remains unrealized. Determination of accurate structures and coordination of bound ligands ...