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Cryoelectron Microscopy

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Ligand identification in CryoEM and X-ray maps using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule li...

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges.

Briefings in bioinformatics
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individua...

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Briefings in bioinformatics
Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the ...

CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs.

Bioinformatics (Oxford, England)
MOTIVATION: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structure...

Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps.

Briefings in bioinformatics
Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity o...

Accounting Conformational Dynamics into Structural Modeling Reflected by Cryo-EM with Deep Learning.

Combinatorial chemistry & high throughput screening
With the continuous development of structural biology, the requirement for accurate threedimensional structures during functional modulation of biological macromolecules is increasing. Therefore, determining the dynamic structures of bio-macromolecul...

An AI-assisted cryo-EM pipeline for structural studies of cellular extracts.

Structure (London, England : 1993)
Proteins, the building blocks of life, often form large assemblies to perform their function but are traditionally studied separately in structural biology. In this issue of Structure, Skalidis et al. (2022) present a workflow to identify members of ...

Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam ...

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning.

Briefings in bioinformatics
With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an...

Applications of deep learning in electron microscopy.

Microscopy (Oxford, England)
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithm...