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Macromolecular Substances

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NMRNet: a deep learning approach to automated peak picking of protein NMR spectra.

Bioinformatics (Oxford, England)
MOTIVATION: Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would ac...

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.

Journal of structural biology
Cryo-EM Single Particle Analysis workflows require tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-...

Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules.

Bioinformatics (Oxford, England)
MOTIVATION: Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecula...

CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks.

Nature methods
Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major ch...

Robotic sample changers for macromolecular X-ray crystallography and biological small-angle X-ray scattering at the National Synchrotron Light Source II.

Journal of synchrotron radiation
Here we present two robotic sample changers integrated into the experimental stations for the macromolecular crystallography (MX) beamlines AMX and FMX, and the biological small-angle scattering (bioSAXS) beamline LiX. They enable fully automated una...

A deep autoencoder framework for discovery of metastable ensembles in biomacromolecules.

The Journal of chemical physics
Biomacromolecules manifest dynamic conformational fluctuation and involve mutual interconversion among metastable states. A robust mapping of their conformational landscape often requires the low-dimensional projection of the conformational ensemble ...

Magneto-Responsive Microneedle Robots for Intestinal Macromolecule Delivery.

Advanced materials (Deerfield Beach, Fla.)
Oral administration is the most convenient and commonly used approach for drug delivery, while it is still a challenge to overcome the complicated gastrointestinal barriers and realize efficient macromolecular drug absorption. Here, novel magneto-res...

Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.

Nature methods
Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and rec...

Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography.

IEEE/ACM transactions on computational biology and bioinformatics
Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecul...

Bayesian deep learning-based H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

Magnetic resonance in medicine
PURPOSE: To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain.