Exploring exchange processes in proteins by paramagnetic perturbation of NMR spectra

2020 ◽  
Vol 22 (11) ◽  
pp. 6247-6259 ◽  
Author(s):  
Yamanappa Hunashal ◽  
Cristina Cantarutti ◽  
Sofia Giorgetti ◽  
Loredana Marchese ◽  
Henriette Molinari ◽  
...  
Keyword(s):  
Intermolecular Interaction ◽  
Nmr Spectra ◽  
Protein Nmr ◽  
Surface Mapping ◽  
Interaction Dynamics ◽  
Exchange Processes ◽  
Paramagnetic Probes

The effects induced by extrinsic paramagnetic probes on protein NMR spectra, widely used for surface mapping, can also be exploited to detect the sites of slow and intermediate exchange due to structural or intermolecular interaction dynamics.

scholarly journals Organoarsenic probes to study proteins by NMR spectroscopy

2021 ◽  
Author(s):  
Mithun Mahawaththa ◽  
Henry Orton ◽  
Ibidolapo Adekoya ◽  
Thomas Huber ◽  
Gottfried Otting ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Nmr Spectroscopy ◽  
Magnetic Resonance ◽  
Nmr Spectra ◽  
Protein Nmr ◽  
Structural Studies ◽  
Pseudocontact Shifts ◽  
Paramagnetic Probes ◽  
Loop Regions ◽  
Nuclear Magnetic

Arsenical probes enable structural studies of proteins. We report the first organoarsenic probes for nuclear magnetic resonance (NMR) spectroscopy to study proteins in solutions. These probes can be attached to irregular loop regions. A lanthanide-binding tag induces sizable pseudocontact shifts in protein NMR spectra of a magnitude never observed for small paramagnetic probes before.

Elimination of13Cα Splitting in Protein NMR Spectra by Deconvolution with Maximum Entropy Reconstruction

2003 ◽  
Vol 125 (9) ◽  
pp. 2382-2383 ◽  
Author(s):  
Nobuhisa Shimba ◽  
Alan S. Stern ◽  
Charles S. Craik ◽  
Jeffrey C. Hoch ◽  
Volker Dötsch
Keyword(s):  
Maximum Entropy ◽  
Nmr Spectra ◽  
Protein Nmr ◽  
Maximum Entropy Reconstruction

Assignment of protein NMR spectra based on projections, multi-way decomposition and a fast correlation approach

2008 ◽  
Vol 42 (2) ◽  
pp. 87-97 ◽  
Author(s):  
Doroteya K. Staykova ◽  
Jonas Fredriksson ◽  
Wolfgang Bermel ◽  
Martin Billeter
Keyword(s):  
Nmr Spectra ◽  
Protein Nmr

Synthesis, structure, and nonrigidity of Os4(CO)15, Os4(CO)13(PMe3)[P(OMe)3], and Os4(CO)14(CNBut)

1995 ◽  
Vol 73 (7) ◽  
pp. 1223-1235 ◽  
Author(s):  
Frederick W.B. Einstein ◽  
Victor J. Johnston ◽  
Andrew K. Ma ◽  
Roland K. Pomeroy
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Nmr Spectra ◽  
Energy Exchange ◽  
Variable Temperature ◽  
Carbonyl Cluster ◽  
Space Group ◽  
Exchange Processes ◽  
Nuclear Magnetic Resonance Spectra ◽  
Osmium Carbonyl ◽  
C Nmr

The binary carbonyl Os4(CO)15, 1, has been prepared by the addition of Os(CO)5 to Os3(CO)10(cyclooctene)2 at −15 °C. The related clusters Os4(CO)13(PMe3)[P(OMe)3], 2, and Os4(CO)14(CNBu′), 3, have been synthesized from Os4(CO)13(PMe3) and Os4(CO)15(CNBu′), respectively. The crystal structures of 1, 2, and 3 reveal similar planar metal skeletons with short (2.774 (1) − 2.793 (2) Å) and long (2.978 (2) − 3.019 (2) Å) peripheral Os—Os bonds; the hinge Os—Os bond in 1–3 ranges in length from 2.936 (2) to 2.948 (1) Å. The variable temperature 13C nuclear magnetic resonance spectra of 1 and 3 show that both are highly nonrigid in solution even at −120 °C. The mechanism of nonrigidity is believed to be an all-equatorial, merry-go-round carbonyl exchange. The variable temperature 13C nmr spectra of 2 indicate it is rigid on the nmr time scale in solution at −45 °C. Carbonyl exchange is, however, observed in the spectrum at −6 °C. From the mode of collapse of the signals it is believed that the lowest energy exchange processes in 3 involve axial-equatorial, merry-go-round CO exchanges in the two planes that each contain a short Os—Os bond. Crystallographic data for compound 1: space group C2/c; a = 12.802 (3) Å, b = 10.217 (3) Å, c = 16.380 (5) Å, β = 91.39 (2)°; R = 0.044, 1204 observed reflections. For compound 2: space group P21/c; a = 11.106 (7) Å, b = 16.931 (5) Å, c = 16.481 (5) Å, β = 97.71 (5)°; R = 0.051, 2117 observed reflections. For compound 3: space group P21/n; a = 11.747 (3) Å, b = 18.009 (5) Å, c = 12.448 (2) Å, β = 92.65 (2)°; R = 0.054, 2131 observed reflections. Keywords: osmium, carbonyl, cluster, nonrigidity.

Computer-aided analysis of protein NMR spectra

1991 ◽  
Vol 1 (6) ◽  
pp. 1036-1041 ◽  
Author(s):  
Jeffrey C. Hoch ◽  
Christina Redfield ◽  
Alan S. Stern
Keyword(s):  
Nmr Spectra ◽  
Protein Nmr ◽  
Computer Aided Analysis ◽  
Computer Aided

scholarly journals Chemical shifts of aromatic protons in protein NMR spectra

FEBS Letters ◽  
1983 ◽  
Vol 159 (1-2) ◽  
pp. 132-136 ◽  
Author(s):  
Christina Redfield ◽  
Jeffrey C. Hoch ◽  
Christopher M. Dobson
Keyword(s):  
Nmr Spectra ◽  
Chemical Shifts ◽  
Protein Nmr

scholarly journals FID-Net: A Versatile Deep Neural Network Architecture for NMR Spectral Reconstruction and Virtual Decoupling

2020 ◽  
Author(s):  
Gogulan Karunanithy ◽  
Flemming Hansen
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Nmr Spectra ◽  
Deep Neural Network ◽  
Protein Nmr ◽  
Single Shot ◽  
Neural Network Architecture ◽  
Diverse Range ◽  
Nmr Data ◽  
Wide Range

<p>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 network architecture inspired by WaveNet, for performing analyses on time domain NMR data. We first demonstrate the effectiveness of this architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra. It is shown that a single network is able to reconstruct a diverse range of 2D NUS spectra that have been obtained with arbitrary sampling schedules, with a range of sweep widths, and a variety of other acquisition parameters. The performance of the trained FID-Net in this case exceeds or matches existing methods currently used for the reconstruction of NUS NMR spectra. Secondly, we present a network based on the FID-Net architecture that can efficiently virtually decouple <sup>13</sup>C<sub>α</sub>-<sup>13</sup>C<sub>β</sub> couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated. The ability for these DNNs to work effectively in a wide range of scenarios, without retraining, paves the way for their widespread usage in analysing NMR data. </p>

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