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- Title
A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection.
- Authors
Li, Tingting; Ren, Xuanbai; Luo, Xiaoli; Wang, Zhuole; Li, Zhenlu; Luo, Xiaoyan; Shen, Jun; Li, Yun; Yuan, Dan; Nussinov, Ruth; Zeng, Xiangxiang; Shi, Junfeng; Cheng, Feixiong
- 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. New approaches to develop antimicrobial agents are urgently needed. In this study, the authors develop a peptide language-based deep generative model to design broad-spectrum antimicrobial peptides against drug-resistant bacteria and validate promising candidates in a wound mouse model.
- Subjects
ANTIMICROBIAL peptides; PEPTIDES; DRUG resistance in microorganisms; GRAM-negative bacteria; SKIN infections; PEPTIDE antibiotics
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-51933-2