Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Jia, Wei; Peng, Jian; Zhang, Yan; Zhu, Jiying; Qiang, Xin; Zhang, Rong; Shi, Lin

doi:10.1016/j.foodres.2023.113640

Food Research International

2023

DOI: 10.1016/j.foodres.2023.113640

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Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Wei Jia,

Jian Peng,

Yan Zhang

et al.

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Cited by 2 publications

References 103 publications

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Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Jia,

Guo,

Bian

et al. 2023

Critical Reviews in Food Science and Nutrition

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Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Jia,

Guo,

Bian

et al. 2023

Critical Reviews in Food Science and Nutrition

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Advanced functional materials as reliable tools for capturing food‐derived peptides to optimize the peptidomics pre‐treatment enrichment workflow

Peng,

Jia,

Zhu

2024

Comp Rev Food Sci Food Safe

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Peptidomics strategies with high throughput, sensitivity, and reproducibility are key tools for comprehensively analyzing peptide composition and potential functional activities in foods. Nevertheless, complex signal interference, limited ionization efficiency, and low abundance have impeded food‐derived peptides’ progress in food detection and analysis. As a result, novel functional materials have been born at the right moment that could eliminate interference and perform efficient enrichment. Of note, few studies have focused on developing peptide enrichment materials for food sample analysis. This work summarizes the development of endogenous peptide, phosphopeptide, and glycopeptide enrichment utilizing materials that have been employed extensively recently: organic framework materials, carbon‐based nanomaterials, bio‐based materials, magnetic materials, and molecularly imprinted polymers. It focuses on the limitations, potential solutions, and future prospects for application in food peptidomics of various advanced functional materials. The size‐exclusion effect of adjustable aperture and the modification of magnetic material enhanced the sensitivity and selectivity of endogenous peptide enrichment and aided in streamlining the enrichment process and cutting down on enrichment time. Not only that, the immobilization of metal ions such as Ti4+ and Nb5+ enhanced the capture of phosphopeptides, and the introduction of hydrophilic groups such as arginine, L‐cysteine, and glutathione into bio‐based materials effectively optimized the hydrophilic enrichment of glycopeptides. Although a portion of the carefully constructed functional materials currently only exhibit promising applications in the field of peptide enrichment for analytical chemistry, there is reason to believe that they will further advance the field of food peptidomics through improved pre‐treatment steps.

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Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Cited by 2 publications

References 103 publications

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Advanced functional materials as reliable tools for capturing food‐derived peptides to optimize the peptidomics pre‐treatment enrichment workflow

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