Identification of novel antibacterial peptides by chemoinformatics and machine learning.
The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.
Fjell
Christopher D
CD
Department of Medicine, Division of Infectious Diseases, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Jenssen
Håvard
H
Hilpert
Kai
K
Cheung
Warren A
WA
Panté
Nelly
N
Hancock
Robert E W
RE
Cherkasov
Artem
A
D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000494:pharmacology; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry* / Q000494:pharmacology; D001185:Artificial Intelligence; D024901:Drug Resistance, Multiple, Bacterial; D016972:Enterobacter cloacae / Q000187:drug effects; D016983:Enterococcus / Q000187:drug effects; D004926:Escherichia coli / Q000187:drug effects; D007711:Klebsiella pneumoniae / Q000187:drug effects; D055624:Methicillin-Resistant Staphylococcus aureus / Q000187:drug effects; D008826:Microbial Sensitivity Tests; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011549:Pseudomonas / Q000187:drug effects; D021281:Quantitative Structure-Activity Relationship
D000900:Anti-Bacterial Agents; D023181:Antimicrobial Cationic Peptides