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深度学习在抗菌肽设计、发现与预测中的应用:现状与展望

  • 王姝 ,
  • 徐春明
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  • (北京工商大学 轻工科学与工程学院,北京,100048)
第一作者:本科生(徐春明副教授为通信作者,E-mail:xucm@th.btbu.edu.cn)

收稿日期: 2024-07-09

  修回日期: 2024-07-25

  网络出版日期: 2024-11-28

基金资助

国家重点研发计划项目(2020YFC1606801)

Application of deep learning in the design, discovery, and prediction of antimicrobial peptides:Current status and prospects

  • WANG Shu ,
  • XU Chunming
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  • (School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China)

Received date: 2024-07-09

  Revised date: 2024-07-25

  Online published: 2024-11-28

摘要

近年来,随着抗生素的过度使用和滥用,微生物耐药性逐渐成为影响人类健康的严重问题;抗菌肽(antimicrobial peptides,AMPs)是一类天然存在的抗菌分子,具有抗菌活性高、广谱活性、种类繁多、可供选择的范围广等优势,且微生物难以产生抗性,因此AMPs被看作是抗生素的有效替代品;然而由于AMPs复杂的结构和多样的序列,从大量的候选肽中识别和筛选AMPs、设计AMPs以及预测不同AMPs的性质十分困难,而通过湿实验的方法挖掘AMPs耗时且费力。目前深度学习技术的发展为AMPs的发现、预测和设计提供了新的途径,本文针对AMPs发现过程中高成本和低效率的问题,总结了深度学习技术在AMPs发现、筛选以及设计中的应用,并进一步总结了深度学习在AMPs预测中的应用。展望未来,随着深度学习技术的不断发展和完善,其在AMPs领域的应用前景将更加广阔,有望加速新型AMPs的研发和应用,为解决抗菌耐药性问题提供新的解决方案。

本文引用格式

王姝 , 徐春明 . 深度学习在抗菌肽设计、发现与预测中的应用:现状与展望[J]. 食品与发酵工业, 2024 , 50(21) : 366 -378 . DOI: 10.13995/j.cnki.11-1802/ts.040439

Abstract

In recent years, with the overuse and abuse of antibiotics, microbial resistance has gradually become a serious problem affecting human health.Antimicrobial peptides (AMPs) are a class of naturally occurring antimicrobial molecules, which have the advantages of high antibacterial activity, broad-spectrum activity, wide variety, and a wide range of options, and it is difficult for microorganisms to develop resistance, so AMPs are regarded as an effective alternative to antibiotics.However, due to the complex structure and diverse sequences of AMPs, it is difficult to identify and screen AMPs from a large number of candidate peptides, design AMPs, and predict the properties of different AMPs, and mining AMPs by wet experiments is time-consuming and laborious.Aiming at the problems of high cost and low efficiency in the process of AMPs discovery, this paper summarizes the application of deep learning technology in the discovery, screening and design of AMPs, and further summarizes the application of deep learning in AMPs prediction.Looking forward to the future, with the continuous development and improvement of deep learning technology, its application prospects in the field of AMPs will be broader, which is expected to accelerate the development and application of new antimicrobial peptides and provide new solutions to solve the problem of antimicrobial resistance.

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