Issue 26, 2021

Machine learning designs non-hemolytic antimicrobial peptides


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 Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.

Graphical abstract: Machine learning designs non-hemolytic antimicrobial peptides

Supplementary files

Article information

Article type
Edge Article
25 Mar 2021
05 Jun 2021
First published
07 Jun 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021,12, 9221-9232

Machine learning designs non-hemolytic antimicrobial peptides

A. Capecchi, X. Cai, H. Personne, T. Köhler, C. van Delden and J. Reymond, Chem. Sci., 2021, 12, 9221 DOI: 10.1039/D1SC01713F

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