scholarly journals Empirical correlation between protein backbone 15N and 13C secondary chemical shifts and its application to nitrogen chemical shift re-referencing

2009 ◽  
Vol 44 (2) ◽  
pp. 95-99 ◽  
Author(s):  
Liya Wang ◽  
John L. Markley
Keyword(s):  
Chemical Shift ◽  
Chemical Shifts ◽  
Empirical Correlation ◽  
Protein Backbone ◽  
Secondary Chemical Shifts ◽  
Secondary Chemical

scholarly journals Bayesian-Maximum-Entropy reweighting of IDP ensembles based on NMR chemical shifts

2019 ◽  
Author(s):  
Ramon Crehuet ◽  
Pedro J. Buigues ◽  
Xavier Salvatella ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Chemical Shift ◽  
Maximum Entropy ◽  
Chemical Shifts ◽  
Intrinsically Disordered Protein ◽  
Intrinsically Disordered ◽  
Computational Data ◽  
Chemical Shift Data ◽  
Secondary Chemical Shifts ◽  
Validation Set ◽  
Secondary Chemical

AbstractBayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

scholarly journals Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 898 ◽  
Author(s):  
Ramon Crehuet ◽  
Pedro J. Buigues ◽  
Xavier Salvatella ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Chemical Shift ◽  
Maximum Entropy ◽  
Chemical Shifts ◽  
Intrinsically Disordered Protein ◽  
Intrinsically Disordered ◽  
Computational Data ◽  
Chemical Shift Data ◽  
Secondary Chemical Shifts ◽  
Validation Set ◽  
Secondary Chemical

Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

scholarly journals Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

2019 ◽  
Vol 73 (12) ◽  
pp. 713-725 ◽  
Author(s):  
Ruth Hendus-Altenburger ◽  
Catarina B. Fernandes ◽  
Katrine Bugge ◽  
Micha B. A. Kunze ◽  
Wouter Boomsma ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Secondary Structure ◽  
Intrinsically Disordered Proteins ◽  
Chemical Shifts ◽  
Random Coil ◽  
Disordered Proteins ◽  
Phosphoryl Group ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Secondary Chemical

Abstract Phosphorylation is one of the main regulators of cellular signaling typically occurring in flexible parts of folded proteins and in intrinsically disordered regions. It can have distinct effects on the chemical environment as well as on the structural properties near the modification site. Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins (IDPs) and the reliability of the analysis depends on an appropriate choice of random coil model. Random coil chemical shifts and sequence correction factors were previously determined for an Ac-QQXQQ-NH2-peptide series with X being any of the 20 common amino acids. However, a matching dataset on the phosphorylated states has so far only been incompletely determined or determined only at a single pH value. Here we extend the database by the addition of the random coil chemical shifts of the phosphorylated states of serine, threonine and tyrosine measured over a range of pH values covering the pKas of the phosphates and at several temperatures (www.bio.ku.dk/sbinlab/randomcoil). The combined results allow for accurate random coil chemical shift determination of phosphorylated regions at any pH and temperature, minimizing systematic biases of the secondary chemical shifts. Comparison of chemical shifts using random coil sets with and without inclusion of the phosphoryl group, revealed under/over estimations of helicity of up to 33%. The expanded set of random coil values will improve the reliability in detection and quantification of transient secondary structure in phosphorylation-modified IDPs.

scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2017 ◽  
Vol 8 (3) ◽  
pp. 2061-2072 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Protein Structure ◽  
Chemical Shift ◽  
Chemical Shifts ◽  
Structure Refinement ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Protein Backbone ◽  
Protein Structure Refinement ◽  
Comparable Accuracy

We show that a QM-based predictor of a protein backbone and CB chemical shifts is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors (errors in chemical shifts shown in red).

Structural information from NMR secondary chemical shifts of peptide α C-H protons in proteins

1983 ◽  
Vol 3 (5) ◽  
pp. 443-452 ◽  
Author(s):  
D. C. Dalgarno ◽  
B. A. Levine ◽  
R. J. P. Williams
Keyword(s):  
Calcium Binding ◽  
Structural Information ◽  
Chemical Shifts ◽  
Troponin C ◽  
Secondary Chemical Shift ◽  
Secondary Chemical Shifts ◽  
Α Helix ◽  
Torsional Angles ◽  
Secondary Chemical ◽  
Β Sheet

The secondary chemical shift experienced by the 1H-NMR resonances of the α C-H protons in proteins can be correlated with their backbone torsional angles ψ, which dictate the orientation of the α C-H proton to the adjacent carbonyl group. It is shown that α C-H protons present in β-sheet regions experience downfield secondary shifts, whereas those in α-helix regions experience upfield secondary shifts. The predictive use of this correlation in assignment studies is illustrated for the calcium-binding protein paravalbumin, for which a crystal structure is available, and troponin C, for which no crystallographic data are available.

scholarly journals Structure of Nanobody Nb23

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3567
Author(s):  
Mathias Percipalle ◽  
Yamanappa Hunashal ◽  
Jan Steyaert ◽  
Federico Fogolari ◽  
Gennaro Esposito
Keyword(s):  
Nmr Spectroscopy ◽  
Chemical Shift ◽  
Solution Structure ◽  
Chemical Shifts ◽  
Molecular Size ◽  
Side Chain ◽  
3D Nmr ◽  
Target Antigen ◽  
Internuclear Distances ◽  
2D And 3D

Background: Nanobodies, or VHHs, are derived from heavy chain-only antibodies (hcAbs) found in camelids. They overcome some of the inherent limitations of monoclonal antibodies (mAbs) and derivatives thereof, due to their smaller molecular size and higher stability, and thus present an alternative to mAbs for therapeutic use. Two nanobodies, Nb23 and Nb24, have been shown to similarly inhibit the self-aggregation of very amyloidogenic variants of β2-microglobulin. Here, the structure of Nb23 was modeled with the Chemical-Shift (CS)-Rosetta server using chemical shift assignments from nuclear magnetic resonance (NMR) spectroscopy experiments, and used as prior knowledge in PONDEROSA restrained modeling based on experimentally assessed internuclear distances. Further validation was comparatively obtained with the results of molecular dynamics trajectories calculated from the resulting best energy-minimized Nb23 conformers. Methods: 2D and 3D NMR spectroscopy experiments were carried out to determine the assignment of the backbone and side chain hydrogen, nitrogen and carbon resonances to extract chemical shifts and interproton separations for restrained modeling. Results: The solution structure of isolated Nb23 nanobody was determined. Conclusions: The structural analysis indicated that isolated Nb23 has a dynamic CDR3 loop distributed over different orientations with respect to Nb24, which could determine differences in target antigen affinity or complex lability.

Circular dichroism spectra of tetraamminecobalt(III) complexes of amino alcohols

1978 ◽  
Vol 31 (11) ◽  
pp. 2399 ◽  
Author(s):  
CJ Hawkins ◽  
GA Lawrance ◽  
JA Palmer
Keyword(s):  
Circular Dichroism ◽  
Chemical Shift ◽  
Chemical Shifts ◽  
Amino Alcohols ◽  
Circular Dichroism Spectra ◽  
Chemical Shift Difference ◽  
Solvent Dependence ◽  
Cotton Effects ◽  
Circular Dichroïsm ◽  
Chiral Amino Alcohols

The circular dichroism spectra are reported for tetraamminecobalt(III) complexes with the chiral amino alcohols 2-aminopropan-1-ol, 2- aminobutan-1-ol, 1-aminopropan-2-ol, 2-amino-1-phenyl-ethanol, ψ- ephedrine and ephedrine with the alcohol groups protonated (OH) and deprotonated (O-). The solvent dependence of the chemical shifts of the NH protons was investigated to determine the effects of stereoselective solvation on the circular dichroism, but, in contrast to some other related systems, the chemical shift difference between the two NH2 protons was relatively insensitive to solvent. Consistent with this, the circular dichroism spectra of the tetraphenylborate salts of the deprotonated complexes were found not to be markedly dependent on solvent. Tetraammine-{(-)-ψ-ephedrine)cobalt(III) and tetraammine{(-)- ephedrine}cobalt(III) were found to have the same signs of Cotton effects for the various d-d transitions, whereas bis{(-)-ψ- ephedrine}copper(II) and bis{(-)-ephedrine}copper(II) had opposite signs. This has been explained in terms of different conformer populations in the cobalt(III) and copper(II) systems.

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