CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters

Developing short, stable, and potent antimicrobial peptides is a promising strategy to combat antibiotic resistance and persistence. We present CAMPER (Constraint-driven AMP Engineering with Ranking), a mechanistic artificial intelligence framework that integrates machine learning with biophysical ranking to prioritize membrane-targeting peptides effective against persister and biofilm forms of methicillin-resistant Staphylococcus aureus. We apply CAMPER to identify WP-CAMPER1 (12mer) that kills S. aureus MW2 at a minimal inhibitory concentration of 4 µg/mL. A 2% topical WP-CAMPER1 formulation reduces S. aureus MW2 burden by 2.5 log<sub>10</sub> (p < 0.0002) in a murine prophylactic skin infection model, while its D-enantiomer, WP-CAMPER1-d, achieves 1.37 log<sub>10</sub> (p < 0.0001) reduction in an established biofilm infection model. Single-cell analysis using a high-throughput microfluidic system shows that WP-CAMPER1-d reduces exponential-phase persisters of S. aureus USA300, and, in a deep-seated murine thigh infection model, decreases stationary-phase S. aureus MW2 persisters by 1.6 log<sub>10</sub> (p < 0.0001).

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Publication Details

Journal
Nature Communications
Published
2026-03-09
DOI
https://doi.org/10.1038/s41467-026-70348-9
Primary Topic
Antimicrobial Peptides and Activities
Type
article
Field-Weighted Citation Impact
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article

CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters

Narchonai Ganesan, Paul P. Sotiriadis, Mandar T. Naik, Youlian Goulev et al.
Nature Communications
Antimicrobial Peptides and Activities
article

CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters

Narchonai Ganesan, Paul P. Sotiriadis, Mandar T. Naik, Youlian Goulev, Charilaos Dellis, Andrew T. Martens, Anindya Basu, M. C. Sherman, Fadi Shehadeh, Li Zhang, Johan Paulsson, Raquel Ferrer-Espada, Biswajit Mishra, Eleftherios Mylonakis, LewisOscar Felix
article en

Abstract

Developing short, stable, and potent antimicrobial peptides is a promising strategy to combat antibiotic resistance and persistence. We present CAMPER (Constraint-driven AMP Engineering with Ranking), a mechanistic artificial intelligence framework that integrates machine learning with biophysical ranking to prioritize membrane-targeting peptides effective against persister and biofilm forms of methicillin-resistant Staphylococcus aureus. We apply CAMPER to identify WP-CAMPER1 (12mer) that kills S. aureus MW2 at a minimal inhibitory concentration of 4 µg/mL. A 2% topical WP-CAMPER1 formulation reduces S. aureus MW2 burden by 2.5 log<sub>10</sub> (p < 0.0002) in a murine prophylactic skin infection model, while its D-enantiomer, WP-CAMPER1-d, achieves 1.37 log<sub>10</sub> (p < 0.0001) reduction in an established biofilm infection model. Single-cell analysis using a high-throughput microfluidic system shows that WP-CAMPER1-d reduces exponential-phase persisters of S. aureus USA300, and, in a deep-seated murine thigh infection model, decreases stationary-phase S. aureus MW2 persisters by 1.6 log<sub>10</sub> (p < 0.0001).

Nature Communications
Harvard University (US), National Technical University of Athens (GR), Rajiv Gandhi Technical University (IN), Cornell University (US), Brown University (US), Methodist Hospital (US), Boston VA Research Institute (US), Athena Research and Innovation Center In Information Communication & Knowledge Technologies (GR), The University of Texas Medical Branch at Galveston (US)
Openalex Percentile: Top 30%
Antimicrobial Peptides and Activities
DOIOpen Access PDFOpenAlex
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