ArticleJournal of biomolecular NMR2013
Chemical shift prediction for denatured proteins.
Article in Journal of biomolecular NMR, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modeling pH-Dependent NMR Chemical Shift Perturbations in Peptides.Biophysical journal · 2019Article
- Structural Characterization of a Heparan Sulfate Pentamer Interacting with LAR-Ig1-2.Biochemistry · 2018Article
- Insights into Unfolded Proteins from the Intrinsic ϕ/ψ Propensities of the AAXAA Host-Guest Series.Biophysical journal · 2016Article
- Mechanisms of amyloid formation revealed by solution NMR.Progress in nuclear magnetic resonance spectroscopy · 2015Review
- pH-dependent random coil (1)H, (13)C, and (15)N chemical shifts of the ionizable amino acids: a guide for protein pK a measurements.Journal of biomolecular NMR · 2014Article
- Sparse labeling of proteins: structural characterization from long range constraints.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2014Review
- Digested disorder: Quarterly intrinsic disorder digest (January/February/March, 2013).Intrinsically disordered proteinsReview
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Authors and funding
6 authors.
Funding
Abstract
While chemical shift prediction has played an important role in aspects of protein NMR that include identification of secondary structure, generation of torsion angle constraints for structure determination, and assignment of resonances in spectra of intrinsically disordered proteins, interest has arisen more recently in using it in alternate assignment strategies for crosspeaks in (1)H-(15)N HSQC spectra of sparsely labeled proteins. One such approach involves correlation of crosspeaks in the spectrum of the native protein with those observed in the spectrum of the denatured protein, followed by assignment of the peaks in the latter spectrum. As in the case of disordered proteins, predicted chemical shifts can aid in these assignments. Some previously developed empirical formulas for chemical shift prediction have depended on basis data sets of 20 pentapeptides. In each case the central residue was varied among the 20 amino common acids, with the flanking residues held constant throughout the given series. However, previous choices of solvent conditions and flanking residues make the parameters in these formulas less than ideal for general application to denatured proteins. Here, we report (1)H and (15)N shifts for a set of alanine based pentapeptides under the low pH urea denaturing conditions that are more appropriate for sparse label assignments. New parameters have been derived and a Perl script was created to facilitate comparison with other parameter sets. A small, but significant, improvement in shift predictions for denatured ubiquitin is demonstrated.
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