AIMC Topic: Macromolecular Substances
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The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions.
Nucleic acids research
Jul 5, 2023
Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitous...
Pre- and Post-publication Verification for Reproducible Data Mining in Macromolecular Crystallography.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
Like an article narrative is deemed by an editor and referees to be worthy of being a version of record on acceptance as a publication, so must the underpinning data also be scrutinized before passing it as a version of record. Indeed without the und...
A deep autoencoder framework for discovery of metastable ensembles in biomacromolecules.
The Journal of chemical physics
Sep 21, 2021
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 ...
Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules.
Bioinformatics (Oxford, England)
Feb 15, 2020
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...
NMRNet: a deep learning approach to automated peak picking of protein NMR spectra.
Bioinformatics (Oxford, England)
Aug 1, 2018
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...
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