High-quality nuclear magnetic resonance (NMR) spectra can be rapidly acquired by combining non-uniform sampling techniques (NUS) with reconstruction algorithms. However, current deep learning (DL) based reconstruction methods focus only on single-domain reconstruction (time or frequency domain), leading to drawbacks like peak loss and artifact peaks and ultimately failing to achieve optimal performance. Moreover, the lack of fully sampled spectra makes it difficult, even impossible, to determine...
[NMR paper] Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction
Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction
Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative...
[NMR paper] DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra
DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra
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. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was...
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09-04-2021 10:34 AM
[NMR paper] FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling
FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling
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 or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a deep neural...
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04-20-2021 12:46 PM
FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling
FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling
Abstract
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 or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR....
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04-19-2021 10:30 AM
Development of a method for reconstruction of crowded NMR spectra from undersampled time-domain data
Development of a method for reconstruction of crowded NMR spectra from undersampled time-domain data
Abstract
NMR is a unique methodology for obtaining information about the conformational dynamics of proteins in heterogeneous biomolecular systems. In various NMR methods, such as transferred cross-saturation, relaxation dispersion, and paramagnetic relaxation enhancement experiments, fast determination of the signal intensity ratios in the NMR spectra with high accuracy is required for analyses of targets with low yields and stabilities. However,...
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02-14-2015 03:52 PM
[NMR paper] Development of a method for reconstruction of crowded NMR spectra from undersampled time-domain data.
Development of a method for reconstruction of crowded NMR spectra from undersampled time-domain data.
Related Articles Development of a method for reconstruction of crowded NMR spectra from undersampled time-domain data.
J Biomol NMR. 2015 Feb 13;
Authors: Ueda T, Yoshiura C, Matsumoto M, Kofuku Y, Okude J, Kondo K, Shiraishi Y, Takeuchi K, Shimada I
Abstract
NMR is a unique methodology for obtaining information about the conformational dynamics of proteins in heterogeneous biomolecular systems. In various NMR methods, such as...
Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment
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