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

Tobias Hegelund Olsen, Betül Yesiltas, Frederikke Isa Marin, Margarita Pertseva, Pedro J. García-Moreno, Simon Gregersen Echers, Michael Toft Overgaard, Charlotte Jacobsen, Ole Lund, Egon Bech Hansen, Paolo Marcatili*

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

63 Citationer (Scopus)
29 Downloads (Pure)

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.

OriginalsprogEngelsk
Artikelnummer21471
TidsskriftScientific Reports
Vol/bind10
Udgave nummer1
ISSN2045-2322
DOI
StatusUdgivet - dec. 2020

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