Machine learning designs non-hemolytic antimicrobial peptides

Capecchi, Alice; Cai, Xingguang; Personne, Hippolyte; Köhler, Thilo; van Delden, Christian; Reymond, Jean-Louis (2021). Machine learning designs non-hemolytic antimicrobial peptides. Chemical Science, 12(26), pp. 9221-9232. The Royal Society of Chemistry 10.1039/D1SC01713F

Preview

Text d1sc01713f.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (945kB) | Preview

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.

Interest & Impact

Downloads

63 since deposited on 19 Jan 2022
44 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item