Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs

Artem Cherkasov, Kai Hilpert, Håvard Jenssen, Christopher D. Fjell, Matt Waldbrook, Sarah C. Mullaly, Rudolf Volkmer, Robert Hancock

Research output: Contribution to journalArticle

221 Citations (Scopus)

Abstract

Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society's most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by capitalizing on accumulating chemical biology information. Using peptide array technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant data was used together with Artificial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the basis of random testing, these models proved remarkably effective in predicting the activity of 100,000 virtual peptides. The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrug-resistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models.

Original languageEnglish
Pages (from-to)65-74
Number of pages10
JournalACS Chemical Biology
Volume4
Issue number1
DOIs
Publication statusPublished - 1 Jan 2009
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine

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