Literature DB >> 31034888

NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins.

Gordon R Chalmers1, Alexander Eletsky1, Laura C Morris1, Jeong-Yeh Yang1, Fang Tian1, Robert J Woods1, Kelley W Moremen1, James H Prestegard2.   

Abstract

Characterization of proteins using NMR methods begins with assignment of resonances to specific residues. Tn class="Chemical">his is usually accomplished using sequential connectivities between nuclear pairs in proteins uniformly labeled with NMR active isotopes. This becomes impractical for larger proteins, and especially for proteins that are best expressed in mammalian cells, including glycoproteins. Here an alternate protocol for the assignment of NMR resonances of sparsely labeled proteins, namely, the ones labeled with a single amino acid type, or a limited subset of types, isotopically enriched with 15N or 13C, is described. The protocol is based on comparison of data collected using extensions of simple two-dimensional NMR experiments (correlated chemical shifts, nuclear Overhauser effects, residual dipolar couplings) to predictions from molecular dynamics trajectories that begin with known protein structures. Optimal pairing of predicted and experimental values is facilitated by a software package that employs a genetic algorithm, ASSIGN_SLP_MD. The approach is applied to the 36-kDa luminal domain of the sialyltransferase, rST6Gal1, in which all phenylalanines are labeled with 15N, and the results are validated by elimination of resonances via single-point mutations of selected phenylalanines to tyrosines. Assignment allows the use of previously published paramagnetic relaxation enhancements to evaluate placement of a substrate analog in the active site of this protein. The protocol will open the way to structural characterization of the many glycosylated and other proteins that are best expressed in mammalian cells.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  genetic algorithm; ligand docking; mammalian cell culture; molecular dynamics; sialyltransferase

Mesh:

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Year:  2019        PMID: 31034888      PMCID: PMC6554063          DOI: 10.1016/j.jmb.2019.04.029

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  41 in total

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Review 4.  Glycan structures and their interactions with proteins. A NMR view.

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