复杂生命体系中痕量糖肽的选择性富集是糖蛋白组学研究的前提和瓶颈。常规材料由于分子间相互作用的限制，在选择性、覆盖度、回收率、重现性等多方面存在严重问题，极大制约了糖蛋白组学的进一步发展。本项目利用多重氢键的可调性和高选择性，结合响应性高分子的可逆构象转变，提出将基于响应性高分子的动态氢键网络引入糖肽富集材料研究中，通过新型糖识别氢键受体的筛选、响应性高分子的分子设计及优化，解决特异性、强结合和可控性之间的矛盾，从而发展出全新的材料体系，以攻克复杂生物样本中糖肽富集分离的瓶颈，推动糖蛋白组学的整体发展。同时，利用材料在不同糖型和不同末端糖的糖肽选择性识别，糖肽吸/脱附动力学控制，及其在新糖基化位点发现中的巨大优势，本项目还将在糖肽分离分析新方法学中取得突破，并与蛋白质组学、生物信息学和分子生物学相关课题组展开合作，在癌症唾液酸糖基化表达异常等领域开展探索性研究，以期将材料推向实际的生物应用。

Selective enrichment of trace glycopeptides from complex biosystems is the challenging premise and bottleneck for glycoproteomics studies. Due to the limitation in intermolecular interactions, conventional enrichment materials usually encounter serious problems in selectivity, coverage, recovery and/or reproducibility, which significantly hamper the development of glycoproteomics. Taking advantages of the adjustability and high selectivity of multiple hydrogen bonds (H-bonds), combining unique feature of reversible conformational transition for smart polymers, we propose here to introduce dynamic H-bonding network based on smart polymers into the study of glycopeptide enrichment materials. Through screening novel H-bonds based carbohydrate receptors, and molecular design and optimization of smart polymers, we try to solve the contradiction among specificity, high affinity and controllability for carbohydrate binding, and thus to develop novel high-performance glycopeptide enrichment materials. Due to the settlement of enrichment bottleneck, this study may largely push forward the development of proteomics. On the other hand, utilizing great advantages of our materials in selective recognition of glycopeptides with different glycoforms and different saccharide terminals, excellent control over adsorption/desorption dynamics, and discovery of new glycosylation sites, we also want to get breakthrough in new methodology for glycopeptide analysis. With these features, we will have cooperation with professional groups in proteomics, bioinformatics and molecular biology to do some exploratory research in areas like abnormal expression of sialylated glycosylation in cancers.