Antimicrobial peptides design by evolutionary multiobjective optimization.

Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particular...

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Autores principales:	Giuseppe Maccari, Mariagrazia Di Luca, Riccardo Nifosí, Francesco Cardarelli, Giovanni Signore, Claudia Boccardi, Angelo Bifone
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b057d0b21e554c8c9c675a947f08cb0d
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spelling	oai:doaj.org-article:b057d0b21e554c8c9c675a947f08cb0d2021-11-18T05:53:36ZAntimicrobial peptides design by evolutionary multiobjective optimization.1553-734X1553-735810.1371/journal.pcbi.1003212https://doaj.org/article/b057d0b21e554c8c9c675a947f08cb0d2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24039565/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement in innate defense. Recently, AMPs have received increasing attention as potential therapeutic agents, owing to their broad activity spectrum and their reduced tendency to induce resistance. The introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. In this work, the possibility to design novel AMP sequences with non-natural amino acids was achieved through a flexible computational approach, based on chemophysical profiles of peptide sequences. Quantitative structure-activity relationship (QSAR) descriptors were employed to code each peptide and train two statistical models in order to account for structural and functional properties of alpha-helical amphipathic AMPs. These models were then used as fitness functions for a multi-objective evolutional algorithm, together with a set of constraints for the design of a series of candidate AMPs. Two ab-initio natural peptides were synthesized and experimentally validated for antimicrobial activity, together with a series of control peptides. Furthermore, a well-known Cecropin-Mellitin alpha helical antimicrobial hybrid (CM18) was optimized by shortening its amino acid sequence while maintaining its activity and a peptide with non-natural amino acids was designed and tested, demonstrating the higher activity achievable with artificial residues.Giuseppe MaccariMariagrazia Di LucaRiccardo NifosíFrancesco CardarelliGiovanni SignoreClaudia BoccardiAngelo BifonePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 9, p e1003212 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Giuseppe Maccari Mariagrazia Di Luca Riccardo Nifosí Francesco Cardarelli Giovanni Signore Claudia Boccardi Angelo Bifone Antimicrobial peptides design by evolutionary multiobjective optimization.
description	Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement in innate defense. Recently, AMPs have received increasing attention as potential therapeutic agents, owing to their broad activity spectrum and their reduced tendency to induce resistance. The introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. In this work, the possibility to design novel AMP sequences with non-natural amino acids was achieved through a flexible computational approach, based on chemophysical profiles of peptide sequences. Quantitative structure-activity relationship (QSAR) descriptors were employed to code each peptide and train two statistical models in order to account for structural and functional properties of alpha-helical amphipathic AMPs. These models were then used as fitness functions for a multi-objective evolutional algorithm, together with a set of constraints for the design of a series of candidate AMPs. Two ab-initio natural peptides were synthesized and experimentally validated for antimicrobial activity, together with a series of control peptides. Furthermore, a well-known Cecropin-Mellitin alpha helical antimicrobial hybrid (CM18) was optimized by shortening its amino acid sequence while maintaining its activity and a peptide with non-natural amino acids was designed and tested, demonstrating the higher activity achievable with artificial residues.
format	article
author	Giuseppe Maccari Mariagrazia Di Luca Riccardo Nifosí Francesco Cardarelli Giovanni Signore Claudia Boccardi Angelo Bifone
author_facet	Giuseppe Maccari Mariagrazia Di Luca Riccardo Nifosí Francesco Cardarelli Giovanni Signore Claudia Boccardi Angelo Bifone
author_sort	Giuseppe Maccari
title	Antimicrobial peptides design by evolutionary multiobjective optimization.
title_short	Antimicrobial peptides design by evolutionary multiobjective optimization.
title_full	Antimicrobial peptides design by evolutionary multiobjective optimization.
title_fullStr	Antimicrobial peptides design by evolutionary multiobjective optimization.
title_full_unstemmed	Antimicrobial peptides design by evolutionary multiobjective optimization.
title_sort	antimicrobial peptides design by evolutionary multiobjective optimization.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/b057d0b21e554c8c9c675a947f08cb0d
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Antimicrobial peptides design by evolutionary multiobjective optimization.

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