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De novo design of anticancer peptides by ensemble artificial neural networks.
Journal article

De novo design of anticancer peptides by ensemble artificial neural networks.

  • Grisoni F Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland. francesca.grisoni@pharma.ethz.ch.
  • Neuhaus CS Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
  • Hishinuma M Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
  • Gabernet G Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
  • Hiss JA Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
  • Kotera M Department of Chemical System Engineering, School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
  • Schneider G Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland. gisbert@ethz.ch.
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  • 2019-04-07
Published in:
  • Journal of molecular modeling. - 2019
Artificial intelligence
Cancer
Counterpropagation
Machine learning
Membranolysis
Peptide design
A549 Cells
Antineoplastic Agents
Cell Proliferation
Humans
MCF-7 Cells
Machine Learning
Models, Molecular
Neoplasms
Neural Networks, Computer
Peptides
English Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/278175
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