Advanced Electron Microscopy Techniques and Applications

This study uses cryo-electron tomography to reveal the molecular structures of two tubulin-based cytoskeleton structures in Toxoplasma gondii, showing their architecture and role in invasion.

This study shows that UVC irradiation at cryogenic temperatures can inactivate pathogens without damaging their structure, making them suitable for cryo-EM analysis.

The paper develops a synthetic Fab module (NabFab) that binds nanobodies with high affinity, enabling high-resolution cryo-EM studies of membrane proteins.

The paper describes a platform integrating structural biology tools to rapidly determine structures of sub-100 kDa protein complexes, enabling efficient biologics discovery and engineering.

This paper introduces DeepFinder, a deep learning method that improves macromolecule identification in 3D cellular cryo-ET data by localizing multiple species with high accuracy.

The paper uses electron cryo-tomography to analyze membrane projections and associated protein complexes in bacterial species, identifying diverse structures and protein associations.

This paper introduces a simple pressure-assisted method for preparing MicroED specimens, enabling control over ice thickness and handling diverse crystal samples.

The paper reports the structure of the LP ring of the bacterial flagellar motor using electron cryomicroscopy, revealing its 26-fold symmetry and intersubunit interactions.

The paper describes a methodology for preparing large-scale digitization samples to enable automated electron microscopy, improving throughput and reducing bias in ultrastructural analysis.

DeepEMhancer improves cryo-EM map quality using deep learning, achieving cleaner and more detailed maps on test cases like SARS-CoV-2 RNA polymerase.

The paper introduces PI-score, a machine learning-based metric to assess protein-protein interfaces in cryo-EM-derived assemblies, identifying errors not captured by density-based methods.

This paper introduces a permeabilization-free protocol for immunohistochemistry in thick tissue sections, enabling correlative microscopy with electron microscopy and two-photon imaging.

The paper introduces hardware and software improvements to enhance cryo-FIB milling for cryo-electron tomography.

This paper introduces DeepMM, a deep learning framework that automatically reconstructs full-atom protein structures from cryo-EM maps with high accuracy and coverage.

The paper demonstrates improved signal-to-noise and resolution in EFTEM elemental mapping by using lower energy-loss N4,5 edges instead of core-loss M4,5 edges for lanthanide detection.

This study evaluates 15 scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography, identifying two robust methods that work across varying signal-to-noise ratios.

DRPnet automatically detects particle centers in cryoEM micrographs using a deep learning approach, achieving comparable or better performance than manual methods with reduced user interaction.

This paper introduces DEFMap, a deep learning method that extracts dynamics information from cryo-EM maps, achieving results comparable to molecular dynamics simulations and experimental approaches.

This paper introduces SuperEM, a deep learning method that improves cryo-EM map resolution, enhancing protein structure modeling.

CryoDRGN uses deep neural networks to reconstruct heterogeneous protein complexes and their motion from cryo-EM data, revealing previously unseen structural states.

DeepTracer is a deep learning method for fast cryo-EM structure modeling of protein complexes, showing improved accuracy and efficiency compared to existing methods.

EM-stellar benchmarks deep learning methods for EM image segmentation, showing performance varies with image properties and no method consistently outperforms others.

This paper introduces a regularisation framework for cryo-EM structure determination using a convolutional neural network trained on known macromolecular structures, showing improved reconstructions on simulated data.

MorphOT interpolates between EM maps using optimal transport to generate morphing trajectories, avoiding blending by displacing densities.

This paper introduces a quantum-based refinement method for cryo-EM maps to improve atomic model accuracy at low resolutions using quantum-chemical restraints.

DeepCryoPicker automates single particle picking in cryo-EM using deep learning, outperforming semi-automated methods without human intervention.

Topaz-Denoise improves cryoEM and cryoET image quality by increasing SNR, enabling faster analysis and solving previously elusive structures.

The paper reports the high-resolution cryo-EM structure of urease from Yersinia enterocolitica, revealing the bimetal active site and enabling drug development insights.

The paper describes a high-throughput transmission electron microscopy pipeline for imaging neuronal circuits, achieving petascale data acquisition of mouse neocortex.

The paper develops a method to estimate membrane curvature from cryo-ET data, showing its validity on benchmark surfaces and application to biological data.