Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice
As antimicrobial resistance rises, new antibacterial candidates are urgently needed. Using sequence space information from over 14,743 functional antimicrobial peptides (AMPs), we improved the antimicrobial properties of citropin 1.1, an AMP with weak antimethicillin resistant Staphylococcus aureus (MRSA) activity, producing a short and potent antistaphylococcal peptide, CIT-8 (13 residues). At 40 μg/mL, CIT-8 eradicated 1 × 108 drug-resistant MRSA and vancomycin resistant S. aureus (VRSA) persister cells within 30 minutes of exposure and reduced the number of viable biofilm cells of MRSA and VRSA by 3 log10 and 4 log10 in established biofilms, respectively. CIT-8 (at 32 μg/mL) depolarized and permeated the S. aureus MW2 membrane. In a mouse model of MRSA skin infection, CIT-8 (2% w/w in petroleum jelly) significantly reduced the bacterial burden by 2.3 log10 (P < 0.0001). Our methodology accelerated AMP design by combining traditional peptide design strategies, such as truncation, substitution, and structure-guided alteration, with machine learning-backed sequence optimization.
Authors
- Felix LewisOscar
- Narchonai Ganesan (ORCID: https://orcid.org/0000-0001-9027-6171)
- Yashpal S. Chhonker (ORCID: https://orcid.org/0000-0001-6455-5388)
- Eleftherios E Mylonakis (ORCID: https://orcid.org/0000-0002-4624-0777)
- Paul P. Sotiriadis (ORCID: https://orcid.org/0000-0001-6030-4645)
- Mandar T. Naik (ORCID: https://orcid.org/0000-0002-7016-572X)
- Biswajit Mishra (ORCID: https://orcid.org/0000-0001-6288-5383)
- Michael B. Sherman (ORCID: https://orcid.org/0000-0002-3680-8066)
- Charilaos Dellis (ORCID: https://orcid.org/0000-0001-6195-0999)
- Anindya Basu (ORCID: https://orcid.org/0000-0001-7196-4614)
- Jianhua Gu (ORCID: https://orcid.org/0000-0002-1578-6814)
- Daryl J. Murry (ORCID: https://orcid.org/0000-0002-4169-5027)
- Sai Sundeep Kollala (ORCID: https://orcid.org/0000-0002-9651-1723)
- Fadi Shehadeh (ORCID: https://orcid.org/0000-0002-9552-4004)
- Frederick M. Ausubel (ORCID: https://orcid.org/0000-0002-6927-8460)
- Liyang Zhang (ORCID: https://orcid.org/0000-0001-8582-4436)
Institutions
- Houston Methodist (US)
- Nebraska Medical Center (US)
- Harvard University (US)
- National Technical University of Athens (GR)
- Rajiv Gandhi Technical University (IN)
- Brown University (US)
- Rajiv Gandhi University of Health Sciences (IN)
- Massachusetts General Hospital (US)
- Rajiv Gandhi University (IN)
- Methodist Hospital (US)
- Athena Research and Innovation Center In Information Communication & Knowledge Technologies (GR)
- The University of Texas Medical Branch at Galveston (US)
- University of Nebraska Medical Center (US)
Publication Details
- Journal
- Journal of Clinical Investigation
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1172/jci185430
- Citations
- 10
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 8.06
Funders
- Brown University
- National Institutes of Health
- Houston Methodist Research Institute
- National Institute of General Medical Sciences