In silico design and optimization of selective membranolytic anticancer peptides

Abstract Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step tow...

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Autores principales:	Gisela Gabernet, Damian Gautschi, Alex T. Müller, Claudia S. Neuhaus, Lucas Armbrecht, Petra S. Dittrich, Jan A. Hiss, Gisbert Schneider
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b8f8603aa1a34be985c452e189d9f671
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spelling	oai:doaj.org-article:b8f8603aa1a34be985c452e189d9f6712021-12-02T15:08:47ZIn silico design and optimization of selective membranolytic anticancer peptides10.1038/s41598-019-47568-92045-2322https://doaj.org/article/b8f8603aa1a34be985c452e189d9f6712019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-47568-9https://doaj.org/toc/2045-2322Abstract Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.Gisela GabernetDamian GautschiAlex T. MüllerClaudia S. NeuhausLucas ArmbrechtPetra S. DittrichJan A. HissGisbert SchneiderNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gisela Gabernet Damian Gautschi Alex T. Müller Claudia S. Neuhaus Lucas Armbrecht Petra S. Dittrich Jan A. Hiss Gisbert Schneider In silico design and optimization of selective membranolytic anticancer peptides
description	Abstract Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.
format	article
author	Gisela Gabernet Damian Gautschi Alex T. Müller Claudia S. Neuhaus Lucas Armbrecht Petra S. Dittrich Jan A. Hiss Gisbert Schneider
author_facet	Gisela Gabernet Damian Gautschi Alex T. Müller Claudia S. Neuhaus Lucas Armbrecht Petra S. Dittrich Jan A. Hiss Gisbert Schneider
author_sort	Gisela Gabernet
title	In silico design and optimization of selective membranolytic anticancer peptides
title_short	In silico design and optimization of selective membranolytic anticancer peptides
title_full	In silico design and optimization of selective membranolytic anticancer peptides
title_fullStr	In silico design and optimization of selective membranolytic anticancer peptides
title_full_unstemmed	In silico design and optimization of selective membranolytic anticancer peptides
title_sort	in silico design and optimization of selective membranolytic anticancer peptides
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/b8f8603aa1a34be985c452e189d9f671
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_version_	1718388022321348608

In silico design and optimization of selective membranolytic anticancer peptides

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