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

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.

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Publication Details

Journal
Nature Communications
Published
2024-08-30
DOI
https://doi.org/10.1038/s41467-024-51933-2
Citations
83
Primary Topic
Antimicrobial Peptides and Activities
Type
article
Field-Weighted Citation Impact
28.61

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article

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

Feixiong Cheng, Junfeng Shi, Ruth Nussinov, Xiangxiang Zeng et al.
83 citations
Nature Communications
Antimicrobial Peptides and Activities
28.61
article

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

Feixiong Cheng, Junfeng Shi, Ruth Nussinov, Xiangxiang Zeng, Zhenlu Li, Jun Shen, Zhuole Wang, Yun Li, Tingting Li, Xiaoyan Luo, Xiaoli Luo, Xuanbai Ren, Dan Yuan
article en
83 citations

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.

Nature CommunicationsVol. 15(1)
Cleveland Clinic (US), Central South University (CN), Tianjin University (CN), Tel Aviv University (IL), Hunan University (CN), Hunan Normal University (CN), Cleveland Clinic Lerner College of Medicine (US), Key Laboratory of Guangdong Province (CN), Frederick National Laboratory for Cancer Research (US), National Cancer Institute (US), Xiangya Hospital Central South University (CN), Case Western Reserve University (US)
U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 0%
Antimicrobial Peptides and Activities
28.61
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