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Cryoelectron Microscopy

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The dynamic duo: cryo-EM teams up with machine learning to visualize biomolecules in motion.

BioTechniques
Cryo-EM has been a key technique in our understanding of biomolecular structures. Now, machine learning techniques are being used to put these structures in motion, revealing dynamic interactions and processes happening on a molecular and cellular le...

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 ...

Cryo-EM images of phase-separated lipid bilayer vesicles analyzed with a machine-learning approach.

Biophysical journal
Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a li...

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.

International journal of molecular sciences
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, whi...

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

Scientific data
The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structu...

RAIN: machine learning-based identification for HIV-1 bNAbs.

Nature communications
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a ...

AlphaFold2 structures guide prospective ligand discovery.

Science (New York, N.Y.)
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules a...

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

Nature communications
The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and...

Smart parallel automated cryo-electron tomography.

Nature methods
In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an ...

Deep Spatio-Temporal Network for Low-SNR Cryo-EM Movie Frame Enhancement.

IEEE/ACM transactions on computational biology and bioinformatics
Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused b...