生物
广谱
抗菌剂
微生物学
多重耐药
抗菌肽
病毒学
抗生素
化学
组合化学
作者
Yanru Li,Xiaofang Xu,Xiaohui Zhang,Zhihui Xu,Jiaqi Zhao,Ruiyu Zhu,Ziyu Wang,Wei Ran,Wenqian Zhao,Na Yan,Yue Leng,Zhiwei Miao,Xiaomin Wang,Liping Wang,Jinxin Liu,Cong Pian,Jinhu Huang
出处
期刊:Gut microbes
[Landes Bioscience]
日期:2025-06-26
卷期号:17 (1): 2523811-2523811
被引量:2
标识
DOI:10.1080/19490976.2025.2523811
摘要
Antimicrobial peptides (AMPs) are promising candidates to address the global antimicrobial resistance crisis, yet their traditional design remains labor-intensive and inefficient. Here, we developed BroadAMP-GPT, an integrated computational-experimental framework that combines AI-driven generation, multi-tiered screening, and experimental validation to rapidly discover potent AMPs with broad-spectrum activity. Using this platform, 57% of AI-generated candidates exhibited potent efficacy against ESKAPE pathogens - key culprits of multidrug-resistant infections. An outstanding candidate, AMP_S13, demonstrated exceptional stability under diverse physiological conditions, including extreme pH (2-10), proteolytic exposure, and elevated temperatures, while maintaining minimal cytotoxicity and low hemolytic activity. AMP_S13 also showed robust in vivo efficacy, reducing mortality in Galleria mellonella infection model and accelerating wound healing in a murine MRSA skin infection model. These results validate BroadAMP-GPT as a transformative tool for accelerating the discovery of stable, broad-spectrum and low-toxicity AMPs, offering a scalable strategy to address the urgent threat of multidrug-resistant pathogens.
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