The predictive accuracy of secondary chemical shifts is more affected by protein secondary structure than solvent environment

2010 ◽  
Vol 46 (4) ◽  
pp. 257-270 ◽  
Author(s):  
Marie-Laurence Tremblay ◽  
Aaron W. Banks ◽  
Jan K. Rainey
Keyword(s):  
Secondary Structure ◽  
Predictive Accuracy ◽  
Chemical Shifts ◽  
Protein Secondary Structure ◽  
Solvent Environment ◽  
Secondary Chemical Shifts ◽  
Secondary Chemical

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 Evolutionary model of protein secondary structure capable of revealing new biological relationships

2019 ◽  
Author(s):  
Jhih-Siang Lai ◽  
Burkhard Rost ◽  
Bostjan Kobe ◽  
Mikael Bodén
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Predictive Accuracy ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Evolutionary Model ◽  
Ancestral Sequences ◽  
Recent Success ◽  
Protein Functions ◽  
Sequence Reconstruction

AbstractAncestral sequence reconstruction has had recent success in decoding the origins and the determinants of complex protein functions. However, phylo­genetic analyses of remote homologues must handle extreme amino-acid se­quence diversity resulting from extended periods of evolutionary change. We exploited the wealth of protein structures to develop an evolutionary model based on protein secondary structure. The approach follows the differences between discrete secondary structure states observed in modern proteins and those hypothesised in their immediate ancestors. We implemented maximum likelihood-based phylogenetic inference to reconstruct ancestral secondary structure. The predictive accuracy from the use of the evolutionary model surpasses that of comparative modelling and sequence-based prediction; the reconstruction extracts information not available from modern structures or the ancestral sequences alone. Based on a phylogenetic analysis of multiple protein families, we showed that the model can highlight relationships that are evolutionarily rooted in structure and not evident in amino acid-based analysis.

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.

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.

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