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 experiments that produce data...
1H R1Ï? relaxation dispersion experiments in aromatic side chains
1H R1Ï? relaxation dispersion experiments in aromatic side chains
Abstract
Aromatic side chains are attractive probes of protein dynamic, since they are often key residues in enzyme active sites and protein binding sites. Dynamic processes on microsecond to millisecond timescales can be studied by relaxation dispersion experiments that attenuate conformational exchange contributions to the transverse relaxation rate by varying the refocusing frequency of applied radio-frequency fields implemented as either CPMG pulse trains or continuous spin-lock...
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09-14-2021 12:38 AM
[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....
[NMR paper] Improved NMR experiments with (13)C-isotropic mixing for assignment of aromatic and aliphatic side chains in labeled proteins.
Improved NMR experiments with (13)C-isotropic mixing for assignment of aromatic and aliphatic side chains in labeled proteins.
http://www.bionmr.com//www.ncbi.nlm.nih.gov/corehtml/query/egifs/http:--production.springer.de-OnlineResources-Logos-springerlink.gif Related Articles Improved NMR experiments with (13)C-isotropic mixing for assignment of aromatic and aliphatic side chains in labeled proteins.
J Biomol NMR. 2014 Jan 4;
Authors: Kovacs H, Gossert A
Abstract
Three improved (13)C-spinlock experiments for side chain assignments of...
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01-07-2014 11:16 PM
4D APSY-HBCB(CG)CDHD experiment for automated assignment of aromatic amino acid side chains in proteins
4D APSY-HBCB(CG)CDHD experiment for automated assignment of aromatic amino acid side chains in proteins
Abstract A four-dimensional (4D) APSY (automated projection spectroscopy)-HBCB(CG)CDHD experiment is presented. This 4D experiment correlates aromatic with aliphatic carbon and proton resonances from the same amino acid side chain of proteins in aqueous solution. It thus allows unambiguous sequence-specific assignment of aromatic amino acid ring signals based on backbone assignments. Compared to conventional 2D approaches, the inclusion of evolution periods on 1Hβ and 13Cδ...
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09-30-2011 08:01 PM
[NMR paper] RESCUE: an artificial neural network tool for the NMR spectral assignment of proteins
RESCUE: an artificial neural network tool for the NMR spectral assignment of proteins.
Related Articles RESCUE: an artificial neural network tool for the NMR spectral assignment of proteins.
J Biomol NMR. 1999 Sep;15(1):15-26
Authors: Pons JL, Delsuc MA
The assignment of the 1H spectrum of a protein or a polypeptide is the prerequisite for advanced NMR studies. We present here an assignment tool based on the artificial neural network technology, which determines the type of the amino acid from the chemical shift values observed in the 1H...
A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins
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