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Cryoelectron Microscopy

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Residue-level error detection in cryoelectron microscopy models.

Structure (London, England : 1993)
Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local back...

PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms.

Journal of structural biology
Particle localization (picking) in digital tomograms is a laborious and time-intensive step in cryogenic electron tomography (cryoET) analysis often requiring considerable user involvement, thus becoming a bottleneck for automated cryoET subtomogram ...

A large expert-curated cryo-EM image dataset for machine learning protein particle picking.

Scientific data
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. Howeve...

Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility.

Cell
Sperm motility is crucial for successful fertilization. Highly decorated doublet microtubules (DMTs) form the sperm tail skeleton, which propels the movement of spermatozoa. Using cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-ba...

3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nanometer range and strong contrast for membranous structures without requiring labeling or chemical fixation. T...

Computational modeling of membrane trafficking processes: From large molecular assemblies to chemical specificity.

Current opinion in cell biology
In the last decade, molecular dynamics (MD) simulations have become an essential tool to investigate the molecular properties of membrane trafficking processes, often in conjunction with experimental approaches. The combination of MD simulations with...

De novo design of protein structure and function with RFdiffusion.

Nature
There has been considerable recent progress in designing new proteins using deep-learning methods. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de no...

AlphaFold and the future of structural biology.

Acta crystallographica. Section D, Structural biology
This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

AlphaFold and the future of structural biology.

IUCrJ
This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

Biomolecular NMR in the AI-assisted structural biology era: Old tricks and new opportunities.

Biochimica et biophysica acta. Proteins and proteomics
Over the last 40 years nuclear magnetic resonance (NMR) spectroscopy has established itself as one of the most versatile techniques for the characterization of biomolecules, especially proteins. Given the molecular size limitations of NMR together wi...