Alice Capecchi, Xingguang Cai, Hippolyte Personne, Thilo Köhler, Christian van Delden, Jean-Louis Reymond
Author Information
Alice Capecchi: Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland jean-louis.reymond@dcb.unibe.ch. ORCID
Xingguang Cai: Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland jean-louis.reymond@dcb.unibe.ch. ORCID
Hippolyte Personne: Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland jean-louis.reymond@dcb.unibe.ch. ORCID
Thilo Köhler: Department of Microbiology and Molecular Medicine, University of Geneva Switzerland.
Christian van Delden: Department of Microbiology and Molecular Medicine, University of Geneva Switzerland.
Jean-Louis Reymond: Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland jean-louis.reymond@dcb.unibe.ch. ORCID
Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against , , and methicillin-resistant (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.
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