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Cryoelectron Microscopy

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Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps.

Scientific reports
Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on a...

Need for Cross-Validation of Single Particle Cryo-EM.

Journal of chemical information and modeling
Cross-validation is used to determine the validity of a model on unseen data by assessing if the model is overfitted to noise. It is widely used in many fields, from artificial intelligence to structural biology in X-ray crystallography and nuclear m...

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Journal of microscopy
Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a tim...

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs.

Nature methods
Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives ...

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Nature methods
Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a...

AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images.

BMC bioinformatics
BACKGROUND: An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, curre...

Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN.

BMC bioinformatics
BACKGROUND: Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large ...

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation.

Journal of structural biology
Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isola...

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PloS one
Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural hete...

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

BMC bioinformatics
BACKGROUND: Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands ...