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Predictions for α-Helical Glycopeptide Design from Structural Bioinformatics Analysis
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    Predictions for α-Helical Glycopeptide Design from Structural Bioinformatics Analysis
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    Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2017, 57, 10, 2598–2611
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    https://doi.org/10.1021/acs.jcim.7b00123
    Published September 27, 2017
    Copyright © 2017 American Chemical Society

    Abstract

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    Glycosylation not only impacts the functions of glycoproteins but can also improve glycoprotein stability and folding efficiency—characteristics that are desirable for protein engineering and therapeutic design. To further elucidate the effects of N-glycosylation on protein structure and to provide principles useful for the rational design of α-helical glycopeptides, we investigate stabilizing protein–sugar interactions in α-helical glycosylation sites using an integrated structural bioinformatics analysis and molecular dynamics simulation approach. We identify two glycan conformations with an Asn χ1 of 180° or 300° that are amenable to α-helical structure in natural α-helical glycosylation sites in the Protein Data Bank. A combination of sterics and favorable intraglycopeptide enthalpy explains the existence of only these two conformations. Furthermore, we catalog all known protein–sugar interactions that utilize these conformational modes. The most common interactions involve either a Glu residue at the −4 position interacting with the GlcNAc whose Asn has χ1 = 300° or a Glu residue at the +4 position interacting with the GlcNAc whose Asn has χ1 = 180°. Via metadynamics simulations of model α-helical glycopeptides with each of these two interactions, we find that both interactions are stabilizing as a result of favorable electrostatic intraglycopeptide interactions. Thus, we suggest that incorporating a Glu at either the −4 or +4 position relative to an N-linked glycan may be a useful strategy for engineering stable α-helical glycoproteins.

    Copyright © 2017 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.7b00123.

    • List of all PDB IDs that contain α-helical glycosylation sites; analysis of GlcNAc ring conformations, dihedral angle distributions, numbers of interacting residues, and distributions of χ2 for three different χ1 conformers of 9-residue, N-terminal, and C-terminal α-helical glycosylation sites in the PDB; dihedral distributions for N-terminal α-helical glycosylation sites with −4 Glu–GlcNAc interactions and for C-terminal α-helical glycosylation sites with +4 Glu–GlcNAc interactions; comparison of −4 and +4 Glu–GlcNAc interaction energies in N- and C-terminal α-helical glycosylation sites; structures of glycosylation sites in the PDB with both −4 and +4 Glu; sequence alignment of the PLBD1 family in Swiss-Prot; complete results from steric analysis of model glycopeptides; free energy profiles, dihedral distributions, and thermodynamics decomposition of model glycopeptide N in α-helical, β-hairpin, and random coil conformations from BE-META simulations; dihedral distributions and thermodynamics decomposition of model glycopeptides EN, NE, and ENE in the α-helical conformation from BE-META simulations; average number of α-helical residues and percent α-helicity per residue for unconstrained MD simulations of N, EN, NE, and ENE and N, EN, NE, and ENE with +2 Thr (PDF)

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    Cited By

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    This article is cited by 2 publications.

    1. Shalini Awasthi, Nisanth N. Nair. Exploring high‐dimensional free energy landscapes of chemical reactions. WIREs Computational Molecular Science 2019, 9 (3) https://doi.org/10.1002/wcms.1398
    2. Chongyang Wu, Huy N. Hoang, Ligong Liu, David P. Fairlie. Glucuronic acid as a helix-inducing linker in short peptides. Chemical Communications 2018, 54 (17) , 2162-2165. https://doi.org/10.1039/C7CC09785A

    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2017, 57, 10, 2598–2611
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jcim.7b00123
    Published September 27, 2017
    Copyright © 2017 American Chemical Society

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