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A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection

Author

Listed:
  • Tingting Li

    (Hunan University
    Hunan University)

  • Xuanbai Ren

    (Hunan University)

  • Xiaoli Luo

    (Hunan University)

  • Zhuole Wang

    (Hunan University
    Hunan University)

  • Zhenlu Li

    (Tianjin University)

  • Xiaoyan Luo

    (Hunan University)

  • Jun Shen

    (Hunan University
    Hunan University)

  • Yun Li

    (The 2nd Xiangya Hospital of Central South University)

  • Dan Yuan

    (Hunan University
    Hunan University)

  • Ruth Nussinov

    (Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute
    Tel Aviv University)

  • Xiangxiang Zeng

    (Hunan University)

  • Junfeng Shi

    (Hunan University
    Hunan University)

  • Feixiong Cheng

    (Lerner Research Institute, Cleveland Clinic
    Lerner Research Institute, Cleveland Clinic
    Case Western Reserve University
    Case Western Reserve University)

Abstract

Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the antimicrobial resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, broad-spectrum AMPs. Using deepAMP to reduce antimicrobial resistance and enhance the membrane-disrupting abilities of AMPs, we identify, synthesize, and experimentally test 18 T1-AMP (Tier 1) and 11 T2-AMP (Tier 2) candidates in a two-round design and by employing cross-optimization-validation. More than 90% of the designed AMPs show a better inhibition than penetratin in both Gram-positive (i.e., S. aureus) and Gram-negative bacteria (i.e., K. pneumoniae and P. aeruginosa). T2-9 shows the strongest antibacterial activity, comparable to FDA-approved antibiotics. We show that three AMPs (T1-2, T1-5 and T2-10) significantly reduce resistance to S. aureus compared to ciprofloxacin and are effective against skin wound infection in a female wound mouse model infected with P. aeruginosa. In summary, deepAMP expedites discovery of effective, broad-spectrum AMPs against drug-resistant bacteria.

Suggested Citation

  • Tingting Li & Xuanbai Ren & Xiaoli Luo & Zhuole Wang & Zhenlu Li & Xiaoyan Luo & Jun Shen & Yun Li & Dan Yuan & Ruth Nussinov & Xiangxiang Zeng & Junfeng Shi & Feixiong Cheng, 2024. "A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51933-2
    DOI: 10.1038/s41467-024-51933-2
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    References listed on IDEAS

    as
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    2. Cassandra Willyard, 2017. "The drug-resistant bacteria that pose the greatest health threats," Nature, Nature, vol. 543(7643), pages 15-15, March.
    3. Josh Abramson & Jonas Adler & Jack Dunger & Richard Evans & Tim Green & Alexander Pritzel & Olaf Ronneberger & Lindsay Willmore & Andrew J. Ballard & Joshua Bambrick & Sebastian W. Bodenstein & David , 2024. "Accurate structure prediction of biomolecular interactions with AlphaFold 3," Nature, Nature, vol. 630(8016), pages 493-500, June.
    4. William F. Porto & Luz Irazazabal & Eliane S. F. Alves & Suzana M. Ribeiro & Carolina O. Matos & Állan S. Pires & Isabel C. M. Fensterseifer & Vivian J. Miranda & Evan F. Haney & Vincent Humblot & Mar, 2018. "In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
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