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.
journal_name
J Mol Modeljournal_title
Journal of molecular modelingauthors
Grisoni F,Neuhaus CS,Hishinuma M,Gabernet G,Hiss JA,Kotera M,Schneider Gdoi
10.1007/s00894-019-4007-6subject
Has Abstractpub_date
2019-04-05 00:00:00pages
112issue
5eissn
1610-2940issn
0948-5023pii
10.1007/s00894-019-4007-6journal_volume
25pub_type
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