In silico design and optimization of selective membranolytic anticancer peptides

Scientific Reports
Gisela GabernetGisbert Schneider

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.

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Citations

Apr 16, 2020·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Mariangela GarofaloJacopo Sgrignani
Sep 20, 2020·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Yamil LiscanoJean Paul Delgado
Oct 8, 2020·Scientific Reports·Fabien PlissonCristina Martínez-Hernández
Dec 15, 2020·The Biochemical Journal·Marco DrexeliusInes Neundorf
Dec 1, 2020·Biomedicine & Pharmacotherapy = Biomédecine & Pharmacothérapie·Patrick Brendan Timmons, Chandralal M Hewage
Dec 24, 2019·Journal of Chemical Information and Modeling·Alice CapecchiJean-Louis Reymond
May 2, 2021·Computational Biology and Chemistry·H M Fazlul HaqueSwakkhar Shatabda
Aug 14, 2020·ACS Combinatorial Science·Alexander T TaguchiNeal W Woodbury

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Methods Mentioned

BETA
circular dichroism
chip

Software Mentioned

modlAMP
AmphiArc2
Learn
HIT
HelicesACP
PEPCATS
Scikit
CD
SME
Python

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