AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides

Abstract Dietary antioxidants are an important preservative in food and have been suggested to help in disease prevention. With consumer demands for less synthetic and safer additives in food products, the food industry is searching for antioxidants that can be marketed as natural. Peptides derived...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tobias Hegelund Olsen, Betül Yesiltas, Frederikke Isa Marin, Margarita Pertseva, Pedro J. García-Moreno, Simon Gregersen, Michael Toft Overgaard, Charlotte Jacobsen, Ole Lund, Egon Bech Hansen, Paolo Marcatili
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a1eed535d43b47a08d50159f7cf19979
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a1eed535d43b47a08d50159f7cf19979
record_format	dspace
spelling	oai:doaj.org-article:a1eed535d43b47a08d50159f7cf199792021-12-02T15:11:52ZAnOxPePred: using deep learning for the prediction of antioxidative properties of peptides10.1038/s41598-020-78319-w2045-2322https://doaj.org/article/a1eed535d43b47a08d50159f7cf199792020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78319-whttps://doaj.org/toc/2045-2322Abstract Dietary antioxidants are an important preservative in food and have been suggested to help in disease prevention. With consumer demands for less synthetic and safer additives in food products, the food industry is searching for antioxidants that can be marketed as natural. Peptides derived from natural proteins show promise, as they are generally regarded as safe and potentially contain other beneficial bioactivities. Antioxidative peptides are usually obtained by testing various peptides derived from hydrolysis of proteins by a selection of proteases. This slow and cumbersome trial-and-error approach to identify antioxidative peptides has increased interest in developing computational approaches for prediction of antioxidant activity and thereby reduce laboratory work. A few antioxidant predictors exist, however, no tool predicting the antioxidative properties of peptides is, to the best of our knowledge, currently available as a web-server. We here present the AnOxPePred tool and web-server ( http://services.bioinformatics.dtu.dk/service.php?AnOxPePred-1.0 ) that uses deep learning to predict the antioxidant properties of peptides. Our model was trained on a curated dataset consisting of experimentally-tested antioxidant and non-antioxidant peptides. For a variety of metrics our method displays a prediction performance better than a k-NN sequence identity-based approach. Furthermore, the developed tool will be a good benchmark for future predictors of antioxidant peptides.Tobias Hegelund OlsenBetül YesiltasFrederikke Isa MarinMargarita PertsevaPedro J. García-MorenoSimon GregersenMichael Toft OvergaardCharlotte JacobsenOle LundEgon Bech HansenPaolo MarcatiliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tobias Hegelund Olsen Betül Yesiltas Frederikke Isa Marin Margarita Pertseva Pedro J. García-Moreno Simon Gregersen Michael Toft Overgaard Charlotte Jacobsen Ole Lund Egon Bech Hansen Paolo Marcatili AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
description	Abstract Dietary antioxidants are an important preservative in food and have been suggested to help in disease prevention. With consumer demands for less synthetic and safer additives in food products, the food industry is searching for antioxidants that can be marketed as natural. Peptides derived from natural proteins show promise, as they are generally regarded as safe and potentially contain other beneficial bioactivities. Antioxidative peptides are usually obtained by testing various peptides derived from hydrolysis of proteins by a selection of proteases. This slow and cumbersome trial-and-error approach to identify antioxidative peptides has increased interest in developing computational approaches for prediction of antioxidant activity and thereby reduce laboratory work. A few antioxidant predictors exist, however, no tool predicting the antioxidative properties of peptides is, to the best of our knowledge, currently available as a web-server. We here present the AnOxPePred tool and web-server ( http://services.bioinformatics.dtu.dk/service.php?AnOxPePred-1.0 ) that uses deep learning to predict the antioxidant properties of peptides. Our model was trained on a curated dataset consisting of experimentally-tested antioxidant and non-antioxidant peptides. For a variety of metrics our method displays a prediction performance better than a k-NN sequence identity-based approach. Furthermore, the developed tool will be a good benchmark for future predictors of antioxidant peptides.
format	article
author	Tobias Hegelund Olsen Betül Yesiltas Frederikke Isa Marin Margarita Pertseva Pedro J. García-Moreno Simon Gregersen Michael Toft Overgaard Charlotte Jacobsen Ole Lund Egon Bech Hansen Paolo Marcatili
author_facet	Tobias Hegelund Olsen Betül Yesiltas Frederikke Isa Marin Margarita Pertseva Pedro J. García-Moreno Simon Gregersen Michael Toft Overgaard Charlotte Jacobsen Ole Lund Egon Bech Hansen Paolo Marcatili
author_sort	Tobias Hegelund Olsen
title	AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
title_short	AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
title_full	AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
title_fullStr	AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
title_full_unstemmed	AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
title_sort	anoxpepred: using deep learning for the prediction of antioxidative properties of peptides
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a1eed535d43b47a08d50159f7cf19979
work_keys_str_mv	AT tobiashegelundolsen anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT betulyesiltas anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT frederikkeisamarin anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT margaritapertseva anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT pedrojgarciamoreno anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT simongregersen anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT michaeltoftovergaard anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT charlottejacobsen anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT olelund anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT egonbechhansen anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides AT paolomarcatili anoxpepredusingdeeplearningforthepredictionofantioxidativepropertiesofpeptides
_version_	1718387649839890432

AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides

Ejemplares similares