NMR Resonance Assignment Methodology: Characterizing Large Sparsely Labeled Glycoproteins

Gordon R. Chalmers, Alexander Eletsky, Laura C. Morris, Jeong Yeh Yang, Fang Tian, Robert J. Woods, Kelley W. Moremen, James H. Prestegard

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Characterization of proteins using NMR methods begins with assignment of resonances to specific residues. This 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.

Original languageEnglish (US)
Pages (from-to)2369-2382
Number of pages14
JournalJournal of Molecular Biology
Volume431
Issue number12
DOIs
StatePublished - May 31 2019

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Molecular Biology

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