ArticleScientific reports2021
Molecular characterization of sequence-driven peptide glycation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Addressing the Problem of Lysine Glycation Prediction in Proteins via Recurrent Neural Networks.BioMed research international · 2025Article
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6 authors.
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Abstract
Peptide glycation is an important, yet poorly understood reaction not only found in food but also in biological systems. The enormous heterogeneity of peptides and the complexity of glycation reactions impeded large-scale analysis of peptide derived glycation products and to understand both the contributing factors and how this affects the biological activity of peptides. Analyzing time-resolved Amadori product formation, we here explored site-specific glycation for 264 peptides. Intensity profiling together with in-depth computational sequence deconvolution resolved differences in peptide glycation based on microheterogeneity and revealed particularly reactive peptide collectives. These peptides feature potentially important sequence patterns that appear in several established bio- and sensory-active peptides from independent sources, which suggests that our approach serves system-wide applicability. We generated a pattern peptide map and propose that in peptide glycation the herein identified molecular checkpoints can be used as indication of sequence reactivity.
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