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Optimization of Antibacterial Peptides by Genetic Algorithms and Cheminformatics

Authors

  • Christopher D. Fjell,

    Corresponding author
    1. Faculty of Medicine, Division of Infectious Diseases, Department of Medicine, University of British Columbia, 2733 Heather Street, Vancouver, BC, V5Z 3J5, Canada
    2. Centre for Microbial Diseases and Immunity Research, University of British Columbia, 2259 Lower Mall, Vancouver, BC, V6T 1Z4, Canada
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  • Håvard Jenssen,

    1. Centre for Microbial Diseases and Immunity Research, University of British Columbia, 2259 Lower Mall, Vancouver, BC, V6T 1Z4, Canada
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  • Warren A. Cheung,

    1. Faculty of Medicine, Division of Infectious Diseases, Department of Medicine, University of British Columbia, 2733 Heather Street, Vancouver, BC, V5Z 3J5, Canada
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  • Robert E. W. Hancock,

    1. Centre for Microbial Diseases and Immunity Research, University of British Columbia, 2259 Lower Mall, Vancouver, BC, V6T 1Z4, Canada
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  • Artem Cherkasov

    1. Prostate Centre at the Vancouver General Hospital, University of British Columbia, 2640 Oak Street, BC, V6H 3Z6, Canada
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Corresponding author: Christopher D. Fjell, cfjell@interchange.ubc.ca

Abstract

Pathogens resistant to available drug therapies are a pressing global health problem. Short, cationic peptides represent a novel class of agents that have lower rates of drug resistance than derivatives of current antibiotics. Previously, we created a software system utilizing artificial neural networks that were trained on quantitative structure-activity relationship descriptors calculated for a total of 1400 synthetic peptides for which antibacterial activity was determined. Using the trained system, we correctly identified additional peptides with activity of 94% accuracy; active peptides were 47 of the top rated 50 peptides chosen from an in silico library of nearly 100 000 sequences. Here, we report a method of generating candidate peptide sequences using the heuristic evolutionary programming method of genetic algorithms (GA), which provided a large (19-fold) improvement in identification of novel antibacterial peptides. Approximately 0.50% of peptides evaluated during the GA method were classified as highly active, while only 0.026% of the nearly 100 000 sequences we previously screened were classified as highly active. A selection of these peptides was tested in vitro and activities reported here. While GA significantly improves the possibility of identifying candidate peptides, we encountered important pitfalls to this method that should be considered when using GA.

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