Title

Crosslinking constraints and computational models as complementary tools in modeling the extracellular domain of the glycine receptor

Citation for published article

Liu, Z., Szarecka, A., Yonkunas, M., Speranskiy, K., Kurnikova, M., & Cascio, M. (2014). Crosslinking Constraints and Computational Models as Complementary Tools in Modeling the Extracellular Domain of the Glycine Receptor. PLoS ONE, 9(7), e102571. https://doi.org/10.1371/journal.pone.0102571

DOI

10.1371/journal.pone.0102571

Peer Reviewed

1

Document Type

Journal Article

School

Bayer School of Natural and Environmental Sciences

Primary Author Department

Chemistry and Biochemistry

Abstract

The glycine receptor (GlyR), a member of the pentameric ligand-gated ion channel superfamily, is the major inhibitory neurotransmitter-gated receptor in the spinal cord and brainstem. In these receptors, the extracellular domain binds agonists, antagonists and various other modulatory ligands that act allosterically to modulate receptor function. The structures of homologous receptors and binding proteins provide templates for modeling of the ligand-binding domain of GlyR, but limitations in sequence homology and structure resolution impact on modeling studies. The determination of distance constraints via chemical crosslinking studies coupled with mass spectrometry can provide additional structural information to aid in model refinement, however it is critical to be able to distinguish between intra- and inter-subunit constraints. In this report we model the structure of GlyBP, a structural and functional homolog of the extracellular domain of human homomeric α1 GlyR. We then show that intra- and intersubunit Lys-Lys crosslinks in trypsinized samples of purified monomeric and oligomeric protein bands from SDS-polyacrylamide gels may be identified and differentiated by MALDI-TOF MS studies of limited resolution. Thus, broadly available MS platforms are capable of providing distance constraints that may be utilized in characterizing large complexes that may be less amenable to NMR and crystallographic studies. Systematic studies of state-dependent chemical crosslinking and mass spectrometric identification of crosslinked sites has the potential to complement computational modeling efforts by providing constraints that can validate and refine allosteric models. © 2014 Liu et al.

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