Medicinal Chemistry & Chemical Biology, Contributed Talk (15min)

Machine Learning Guides the Design of Non-Hemolytic Antimicrobial Peptides

A. Capecchi1, X. Cai1, H. Personne1, T. Köhler2,3, C. van Delden2,3, J. L. Reymond1*
1Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland, 2Department of Microbiology and Molecular Medicine, University of Geneva, 3Service of Infectious Diseases, University Hospital of Geneva, Geneva, Switzerland

Antimicrobial peptides (AMPs) offer an opportunity to address antibiotic resistance, which represents one of the major global public health threats [1]. Most AMPs are membrane disruptive amphiphiles, and unfortunately this activity is often associated with toxicity to human red blood cells [2]. Here, we have trained a combination of supervised and unsupervised recurrent neural networks (RNN) using hemolysis and activity data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) [3] to design non-hemolytic AMPs. The synthesis and test of 28 generated peptides led to the identification of eight novel non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA).

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[2] Greco, I.; Molchanova, N.; Holmedal, E.; Jenssen, H.; Hummel, B. D.; Watts, J. L.; Håkansson, J.; Hansen, P. R.; Svenson, J. Correlation between Hemolytic Activity, Cytotoxicity and Systemic in Vivo Toxicity of Synthetic Antimicrobial Peptides. Sci. Rep. 2020, 10 (1), 13206.
[3] Gogoladze, G.; Grigolava, M.; Vishnepolsky, B.; Chubinidze, M.; Duroux, P.; Lefranc, M.-P.; Pirtskhalava, M. DBAASP: Database of Antimicrobial Activity and Structure of Peptides. FEMS Microbiol. Lett. 2014, 357 (1), 63–68.