Literature DB >> 30290036

A Systematic Study of Selective Protein Glycation.

Nicole M Sjoblom1, Maxfield M G Kelsey2, Rebecca A Scheck2,1.   

Abstract

Glycation is a non-enzymatic post-translational modification (PTM) that remains poorly understood, largely because it is unknown how it occurs selectively. Using mass spectrometry, it was possible to evaluate total glycation levels, identify distinct glycated products, assign unique glycation sites, and correlate these data with chemical and structural features for a panel of proteins glycated in vitro. It was determined that the extent of glycation does not correlate with pKa or surface exposure at reactive sites. Rather, the data reveal that primary sequence dictates the overall likelihood that a site will become glycated, while surrounding structure further sculpts the glycation outcome. Clustered acidic residues were found to prevent glycation, whereas a combination of tyrosine and polar residues appear to promote glycation. This work contributes important new knowledge about the molecular features that govern selective glycation.
© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  advanced glycation end-product; chemoselectivity; glycation; methylglyoxal; protein modification

Mesh:

Substances:

Year:  2018        PMID: 30290036     DOI: 10.1002/anie.201810037

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  3 in total

1.  Comprehensive Analysis of Protein Glycation Reveals Its Potential Impacts on Protein Degradation and Gene Expression in Human Cells.

Authors:  Fangxu Sun; Suttipong Suttapitugsakul; Haopeng Xiao; Ronghu Wu
Journal:  J Am Soc Mass Spectrom       Date:  2019-05-09       Impact factor: 3.109

2.  Synergistic sequence contributions bias glycation outcomes.

Authors:  Joseph M McEwen; Sasha Fraser; Alexxandra L Sosa Guir; Jaydev Dave; Rebecca A Scheck
Journal:  Nat Commun       Date:  2021-06-03       Impact factor: 14.919

3.  On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks.

Authors:  Ulices Que-Salinas; Dulce Martinez-Peon; Angel D Reyes-Figueroa; Ivonne Ibarra; Christian Quintus Scheckhuber
Journal:  Sensors (Basel)       Date:  2022-07-13       Impact factor: 3.847

  3 in total

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