AIMC Topic: Nuclear Magnetic Resonance, Biomolecular
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Biomolecular NMR in the AI-assisted structural biology era: Old tricks and new opportunities.
Biochimica et biophysica acta. Proteins and proteomics
Aug 11, 2023
Over the last 40 years nuclear magnetic resonance (NMR) spectroscopy has established itself as one of the most versatile techniques for the characterization of biomolecules, especially proteins. Given the molecular size limitations of NMR together wi...
Water irradiation devoid pulses enhance the sensitivity of H,H nuclear Overhauser effects.
Journal of biomolecular NMR
Dec 19, 2022
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...
NMR spectrum reconstruction as a pattern recognition problem.
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 24, 2022
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of...
Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA.
Nature communications
Oct 18, 2022
Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, ...
Design and applications of water irradiation devoid RF pulses for ultra-high field biomolecular NMR spectroscopy.
Physical chemistry chemical physics : PCCP
Aug 10, 2022
Water suppression is of paramount importance for many biological and analytical NMR spectroscopy applications. Here, we report the design of a new set of binomial-like radio frequency (RF) pulses that elude water irradiation while exciting or refocus...
Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.
Journal of biomolecular NMR
May 27, 2022
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...
PHRONESIS: A One-Shot Approach for Sequential Assignment of Protein Resonances by Ultrafast MAS Solid-State NMR Spectroscopy.
Chemphyschem : a European journal of chemical physics and physical chemistry
May 19, 2022
Solid-state NMR (ssNMR) spectroscopy has emerged as the method of choice to analyze the structural dynamics of fibrillar, membrane-bound, and crystalline proteins that are recalcitrant to other structural techniques. Recently, H detection under fast...
Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.
Journal of biomolecular NMR
Apr 7, 2022
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...
DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.
Nature communications
Sep 1, 2021
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Her...
FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.
Journal of biomolecular NMR
Apr 19, 2021
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...
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