Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 38899839

Abstract

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs ("dark matter") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.

Authors

  • Yue Zhang
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Runze Li
    Department of Statistics, The Pennsylvania State University, University Park, PA 16802, United States. Electronic address: rzli@psu.edu.
  • Geng Zou
    National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.
  • Yating Guo
    National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.
  • Renwei Wu
    National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.
  • Yang Zhou
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
  • Huanchun Chen
    State Key Laboratory of Agricultural Microbiology, Wuhan, China.
  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.
  • Rob Lavigne
    Department of Biosystems, KU Leuven, Leuven, Belgium.
  • Phillip J Bergen
    Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Jinquan Li
    National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China.

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