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<p>Antimicrobial peptides (AMPs) are short sequences of amino acids, typically 6–50 residues long, that serve as a natural defense mechanism against viruses, cancer, and other pathogens. They function primarily by disrupting microbial membranes, modulating immune responses, or targeting intracellular processes, making them promising alternatives to traditional antibiotics amid rising antimicrobial resistance. While AMPs hold significant promise in addressing infectious diseases and cancerous growths, a number of hurdles exist which necessitate resolution to achieve their full functional capacity. Challenges like high toxicity, poor stability, limited cellular penetration, and costly synthesis have limited their clinical approval. This necessitates the critical prediction and sophisticated design of new AMPs. Modern advancements in deep learning have catalyzed a heightened focus on computational strategies for identifying peptide-based therapeutics. Furthermore, the utilization of emerging peptides, such as those derived from bacterial and fungal metabolites, constitutes a significant factor in this endeavor. In addition, it addresses the importance of advanced methodologies in the creation and exploration of novel antiviral and anticancer peptides. In this review, types of antiviral and anticancer peptides and their structures were discussed. Also, with a modern perspective, the involvement of artificial intelligence and microbial metabolites in reducing the antimicrobial limitations of peptides was studied.</p>

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Antimicrobial peptides for anticancer and antiviral therapy: last promising update

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月29日

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  • Aisa Bahar,
  • Mahyar Porbaran,
  • Meisam Khazaei,
  • Hamed Tahmasebi

摘要

Antimicrobial peptides (AMPs) are short sequences of amino acids, typically 6–50 residues long, that serve as a natural defense mechanism against viruses, cancer, and other pathogens. They function primarily by disrupting microbial membranes, modulating immune responses, or targeting intracellular processes, making them promising alternatives to traditional antibiotics amid rising antimicrobial resistance. While AMPs hold significant promise in addressing infectious diseases and cancerous growths, a number of hurdles exist which necessitate resolution to achieve their full functional capacity. Challenges like high toxicity, poor stability, limited cellular penetration, and costly synthesis have limited their clinical approval. This necessitates the critical prediction and sophisticated design of new AMPs. Modern advancements in deep learning have catalyzed a heightened focus on computational strategies for identifying peptide-based therapeutics. Furthermore, the utilization of emerging peptides, such as those derived from bacterial and fungal metabolites, constitutes a significant factor in this endeavor. In addition, it addresses the importance of advanced methodologies in the creation and exploration of novel antiviral and anticancer peptides. In this review, types of antiviral and anticancer peptides and their structures were discussed. Also, with a modern perspective, the involvement of artificial intelligence and microbial metabolites in reducing the antimicrobial limitations of peptides was studied.