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

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number7538
JournalNature Communications
Volume15
Issue number1
DOIs
StatePublished - Dec 2024

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