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


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Article Info

DATE

2019-05

AUTHORS

Francesca Grisoni, Claudia S Neuhaus, Miyabi Hishinuma, Gisela Gabernet, Jan A Hiss, Masaaki Kotera, Gisbert Schneider

ABSTRACT

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. More... »

PAGES

112

References to SciGraph publications

  • 2013-12. In Silico Models for Designing and Discovering Novel Anticancer Peptides in SCIENTIFIC REPORTS
  • 2015-03. ACPP: A Web Server for Prediction and Design of Anti-cancer Peptides in INTERNATIONAL JOURNAL OF PEPTIDE RESEARCH AND THERAPEUTICS
  • 1988-10. Genetic Algorithms and Machine Learning in MACHINE LEARNING
  • 1989. Self-Organization and Associative Memory in NONE
  • 2005-04. Host defense peptides as new weapons in cancer treatment in CELLULAR AND MOLECULAR LIFE SCIENCES
  • 1996-08. Bagging predictors in MACHINE LEARNING
  • 1964-06. Nonmetric multidimensional scaling: A numerical method in PSYCHOMETRIKA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00894-019-4007-6

    DOI

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    DIMENSIONS

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    PUBMED

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