AI Medical Compendium Journal:
Journal of biomolecular NMR

Showing 1 to 9 of 9 articles

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Journal of biomolecular NMR
Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR paramete...

Prediction of order parameters based on protein NMR structure ensemble and machine learning.

Journal of biomolecular NMR
The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. Th...

Water irradiation devoid pulses enhance the sensitivity of H,H nuclear Overhauser effects.

Journal of biomolecular NMR
The nuclear Overhauser effect (NOE) is one of NMR spectroscopy's most important and versatile parameters. NOE is routinely utilized to determine the structures of medium-to-large size biomolecules and characterize protein-protein, protein-RNA, protei...

Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.

Journal of biomolecular NMR
Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structur...

Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.

Journal of biomolecular NMR
Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) desig...

FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.

Journal of biomolecular NMR
In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies o...

Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.

Journal of biomolecular NMR
Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on b...

NMR-STAR: comprehensive ontology for representing, archiving and exchanging data from nuclear magnetic resonance spectroscopic experiments.

Journal of biomolecular NMR
The growth of the biological nuclear magnetic resonance (NMR) field and the development of new experimental technology have mandated the revision and enlargement of the NMR-STAR ontology used to represent experiments, spectral and derived data, and s...

Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression.

Journal of biomolecular NMR
The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the (1)H NMR and (13)C chemical shifts reported for non-exchangeable protons of 187 of these R...