合成生物学 ›› 2025, Vol. 6 ›› Issue (2): 461-478.DOI: 10.12211/2096-8280.2024-067

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功能肽合成和挖掘策略研究进展

汤传根1,2,3,4, 王璟1, 张烁1, 张昊宁1, 康振2,4   

  1. 1.南京汉欣医药科技有限公司,江苏 南京 210033
    2.江南大学,未来食品科学中心,江苏 无锡 214122
    3.江南大学生物工程学院,工业生物技术教育部重点实验室,江苏 无锡 214122
    4.江南大学生物工程学院,糖化学与生物技术教育部重点实验室,江苏 无锡 214122
  • 收稿日期:2024-08-28 修回日期:2024-11-11 出版日期:2025-04-30 发布日期:2025-05-20
  • 通讯作者: 汤传根
  • 作者简介:汤传根(1985—),男,博士研究生,工程师。研究方向为重组多肽/蛋白药物、合成多肽药物、功能肽研究、合成生物学产业化应用。E-mail:rootyt@hanxinpharm.com

Advances in synthesis and mining strategies for functional peptides

TANG Chuan′gen1,2,3,4, WANG Jing1, ZHANG Shuo1, ZHANG Haoning1, KANG Zhen2,4   

  1. 1.Nanjing Hanxin Pharmaceutical Technology Co. ,Ltd. ,Nanjing 210033,Jiangsu,China
    2.Science Center for Future Foods,Jiangnan University,Wuxi 214122,Jiangsu,China
    3.Key Laboratory of Industrial Biotechnology,Ministry of Education,School of Biotechnology,Jiangnan University,Wuxi 214122,Jiangsu,China
    4.Key Laboratory of Carbohydrate Chemistry and Biotechnology,Ministry of Education,School of Biotechnology,Jiangnan University,Wuxi 214122,Jiangsu,China
  • Received:2024-08-28 Revised:2024-11-11 Online:2025-04-30 Published:2025-05-20
  • Contact: TANG Chuan′gen

摘要:

功能肽是由2~50个氨基酸组成的短链肽,近年来因其特异性强、作用迅速及副作用低而成为开发新药和功能原料的重要研究热点。首先,本文梳理了功能肽的分类、作用机制及应用场景,总结了不同类型功能肽的特点和在生物医药、食品科学及化妆品等领域的应用。接着,针对功能肽的合成方法,探讨了化学合成与生物合成的最新进展,比较了这两种制备工艺的优缺点以及各自的适用场景。在功能肽挖掘策略方面,本文综述了噬菌体表面展示技术、机器学习算法、分子对接技术及人工智能技术等方面的最新研究,这些技术在功能肽的筛选和设计中展现出重要潜力,提升了研究的效率与准确性。展望未来,功能肽的研究将面临新的挑战与机遇。如何改进合成工艺以提高效率,如何通过结构修饰提高功能肽稳定性,以及如何利用计算机辅助优化和人工智能设计多功能肽,将成为重要的研究方向。同时,加强功能肽的安全性和有效性的评估能进一步提升功能肽的应用潜力。

关键词: 功能肽, 合成生物学, 生物合成, 高通量, 机器学习

Abstract:

Functional peptides are short chain peptides composed of 2 to 50 amino acids, and their biological activities are closely related to their amino acid sequences, chain length, and structural architectures. Functional peptides can play a regulatory role in a variety of physiological processes by specifically recognizing and binding to target molecules in vivo. Due to their rapid action, strong specificity, less side effect and toxicity, functional peptides have shown great application potentials in many fields such as biomedicine, food science and cosmetics. For example, in the field of biomedicine, functional peptides can be used as the basic material of antimicrobe, anticancer, immune regulation and other therapeutic factors. In the food industry, they are used as natural supplements to enhance nutritional value for health benefit. In the field of cosmetics, functional peptides are widely used for the anti-aging, moisturizing, and repairing of the skin. In this paper, we discuss the ways of obtaining functional peptides, mainly including protein hydrolysis, chemical synthesis, and biosynthesis (e.g., through microbial recombinant expression technology), and compare their advantages and disadvantages and respective application scenarios. In terms of strategies for mining functional peptides, we review the latest research progress including phage surface display, machine learning algorithm, molecular docking and artificial intelligence. These techniques show significant potentials in the screening and design of functional peptides. In recent years, the rapid development of synthetic biology and the wide applications of bioinformatics and artificial intelligence have provided new ideas and strategies for the discovery and optimization of functional peptides, making it possible to screen functional peptides through machine learning and high throughput. Looking forward to the future, the research of functional peptides will face new challenges and opportunities. Improving the synthesis process for high efficiency, improving the stability of functional peptides through structural modifications, and using computer-aided optimization and artificial intelligence to design multifunctional peptides will become important research directions. At the same time, strengthening the safety and efficacy assessment of functional peptides can further enhance the applications of functional peptides.

Key words: functional peptide, synthetic biology, biosynthesis, high throughput, machine learning

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