磷酸肽
化学
纳米技术
肽
生化工程
材料科学
生物化学
工程类
作者
Jian Peng,Wei Jia,Jiying Zhu
标识
DOI:10.1111/1541-4337.13395
摘要
Abstract 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 Ti 4+ and Nb 5+ 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI