[NMR paper] A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins
A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins
Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural network (DNN), which transforms NMR spectra recorded on simple uniformly ^(13)C-labeled samples to yield high-quality ¹H-^(13)C correlation maps of aromatic side chains. Key to the success of the DNN is the design of NMR...
[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...
nmrlearner
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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....
Combined NMR and Deep Neural Network approach enhances resolution of protein aromatic side chains - University College London
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