Antimicrobial Peptide Developed with Machine Learning Sequence Optimization Targets Drug Resistant Staphylococcus aureus in Mice

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Abstract

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 anti-methicillin resistant Staphylococcus aureus (MRSA) activity, producing a short and potent anti-staphylococcal peptide, CIT-8 (13 residues). At 40 μg/ml, CIT-8 eradicated 1 × 108 drug-resistant MRSA and VRSA (vancomycin resistant S. aureus) persister cells within 30 mins 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 accelerates AMP design by combining traditional peptide design strategies, such as truncation, substitution, and structure-guided alteration, with machine learning (ML)-backed sequence optimization.

Authors

Biswajit Mishra, Anindya Basu, Fadi Shehadeh, LewisOscar Felix, Sai Sundeep Kollala, Yashpal Singh Chhonker, Mandar T. Naik, Charilaos Dellis, Liyang Zhang, Narchonai Ganesan, Daryl J. Murry, Jianhua Gu, Michael B. Sherman, Frederick M. Ausubel, Paul P. Sotiriadis, Eleftherios Mylonakis