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

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 log₁₀ (p < 0.0002) in a murine prophylactic skin infection model, while its D-enantiomer, WP-CAMPER1-d, achieves 1.37 log₁₀ (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 log₁₀ (p < 0.0001).