AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Cryoelectron Microscopy

Showing 21 to 30 of 114 articles

Clear Filters

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score.

Journal of structural biology
Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality ...

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions.

Journal of structural biology
Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a se...

Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure.

Biophysical journal
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achie...

Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information.

Journal of structural biology
Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that ha...

Automated model building and protein identification in cryo-EM maps.

Nature
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs. Here we present ModelAngelo, a machine-learning approa...

Computational drug development for membrane protein targets.

Nature biotechnology
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine ...

A suite of designed protein cages using machine learning and protein fragment-based protocols.

Structure (London, England : 1993)
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, se...

An RNA origami robot that traps and releases a fluorescent aptamer.

Science advances
RNA nanotechnology aims to use RNA as a programmable material to create self-assembling nanodevices for application in medicine and synthetic biology. The main challenge is to develop advanced RNA robotic devices that both sense, compute, and actuate...

DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning.

Nature communications
To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their tech...

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...