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Combining Evolutionary Covariance and NMR Data for Protein Structure Determination.

Authors :
Huang YJ
Brock KP
Ishida Y
Swapna GVT
Inouye M
Marks DS
Sander C
Montelione GT
Source :
Methods in enzymology [Methods Enzymol] 2019; Vol. 614, pp. 363-392. Date of Electronic Publication: 2018 Dec 23.
Publication Year :
2019

Abstract

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.<br /> (© 2019 Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1557-7988
Volume :
614
Database :
MEDLINE
Journal :
Methods in enzymology
Publication Type :
Academic Journal
Accession number :
30611430
Full Text :
https://doi.org/10.1016/bs.mie.2018.11.004