Advances in artificial design and deep learning for antimicrobial peptide modification strategies

  • XU Haoran ,
  • BI Chongpeng ,
  • WANG Jiajun ,
  • SHAN Anshan ,
  • FENG Xingjun
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  • (College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China)

Received date: 2025-01-20

  Revised date: 2025-02-21

  Online published: 2025-08-04

Abstract

In recent years, infections caused by multi-drug-resistant pathogens have escalated, posing a significant global public health threat.Antimicrobial peptides (AMPs) offer a promising alternative to combat antibiotic resistance due to their unique mechanisms of action.However, their clinical application is hindered by challenges, such as poor stability, limited activity, and high production costs.Various strategies have been explored to improve AMPs, including structural optimization, target-specific design, activity improvement, and refining production processes.Additionally, the integration of deep learning techniques has introduced efficient approaches for the design and optimization of AMP sequences.By predicting antimicrobial activity and optimizing key parameters, these technologies significantly improve research and development efficiency, providing new opportunities for the advancement and clinical applications of antimicrobial peptides.

Cite this article

XU Haoran , BI Chongpeng , WANG Jiajun , SHAN Anshan , FENG Xingjun . Advances in artificial design and deep learning for antimicrobial peptide modification strategies[J]. Food and Fermentation Industries, 2025 , 51(13) : 362 -368 . DOI: 10.13995/j.cnki.11-1802/ts.042190

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