Literature DB >> 35507105

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation.

Wei-Tse Hsu1, Dominique A Ramirez2, Tarek Sammakia3, Zhongping Tan4, Michael R Shirts5.   

Abstract

Insulin has been commonly adopted as a peptide drug to treat diabetes as it facilitates the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin's proteolytic stability and reduce its dimerization propensity, but understanding such phenomena at the molecular level is still difficult. To address this challenge, we proposed and tested several structural determinants that could potentially influence insulin's proteolytic stability and dimerization propensity. We used these metrics to assess the properties of interest from [Formula: see text] aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, respectively, as was in part the solvent-accessible surface area of proteolytic sites. However, other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation influences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Keywords:  Insulin; Molecular dynamics; O-glycosylation; Protein degradation; Stability

Mesh:

Substances:

Year:  2022        PMID: 35507105     DOI: 10.1007/s10822-022-00453-6

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   4.179


  52 in total

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Journal:  Ther Deliv       Date:  2013-11
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