藤壶胶蛋白仿生医用粘合剂研究进展与人工智能赋能展望
Research progress of barnacle cement protein based biomimetic medical adhesives and prospects for artificial intelligence empowerment
胡碧茹 1李卫 2宋俊祎1
作者信息
- 1. 国防科技大学理学院
- 2. 陆军军医大学第一附属医院
- 折叠
摘要
组织损伤临床修复对湿界面粘结能力强、生物相容性优异的医用粘合剂存在迫切需求。海洋生物藤壶、贻贝等能够在水环境下实现牢固粘附，其分泌的粘附蛋白为高性能生物医用粘合剂开发提供宝贵仿生原型。传统研究主要依靠天然胶提取、重组全长蛋白表达、化学高分子仿生等手段开发仿生粘附材料，但面临蛋白表达纯化困难、材料湿粘附性能与生物相容性难以兼顾等瓶颈。合成生物学推动多功能模块融合蛋白粘合剂快速发展，而海量多肽蛋白功能模块的组合筛选成为制约该方向发展的关键难题。机器学习结合多肽定量构效关系，能够挖掘多肽序列特征与湿粘附功能之间的内在关联，为粘附功能多肽模块高通量筛选、最优功能模块组合挖掘提供全新技术手段。本文梳理藤壶胶蛋白的研究现状，总结融合蛋白粘合剂的构建策略，阐述机器学习在粘附多肽、粘附蛋白筛选优化中的应用潜力，归纳该领域现存挑战，为新型医用粘合剂研发提供参考。
Abstract
Tissue repair urgently requires medical adhesives with robust wet?interface adhesion and excellent biocompatibility. Marine organisms including barnacles and mussels exhibit strong adhesion in aqueous environments, and their adhesive proteins serve as valuable biomimetic prototypes for high?performance biomedical adhesives. Traditional strategies to fabricate biomimetic adhesives, such as natural cement extraction, recombinant full?length?protein expression and chemical polymer bionics, face major bottlenecks: troublesome protein expression?purification and a trade?off between wet?adhesion performance and biocompatibility. While synthetic biology has boosted fusion?protein adhesives, combinatorial screening of enormous peptide?protein functional modules limits further advances in this field. Combined with peptide quantitative structure?activity relationships, machine learning can uncover inherent links between peptide sequence features and wet?adhesive functions, enabling high?throughput screening of adhesive?peptide modules and discovery of optimal module combinations. This review summarizes barnacle?cement?protein research progress, outlines construction strategies for fusion?protein adhesives, demonstrates machine?learning potential for adhesive peptide?protein screening and optimization, and highlights remaining challenges, providing guidance for developing novel medical adhesives.关键词
医用粘合剂/藤壶胶蛋白/仿生融合蛋白/机器学习Key words
medical adhesive/barnacle cement protein/biomimetic fusion protein/machine learning引用本文复制引用
胡碧茹,李卫,宋俊祎.藤壶胶蛋白仿生医用粘合剂研究进展与人工智能赋能展望[EB/OL].(2026-09-29)[2026-10-01].http://www.paper.edu.cn/releasepaper/content/202609-33.学科分类
临床医学
