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.

In this work, we developed a novel approach combining bioinformatics, testing of functionality and bottom-up proteomics to obtain peptide emulsifiers from potato side-streams. This is a significant advancement in the process to obtain emulsifier peptides and it is applicable to any type of protein.Our results indicated that structure at the interface is the major determining factor of the emulsifying activity of peptide emulsifiers. Fish oil-in-water emulsions with high physical stability were stabilized with peptides to be predicted to have facial amphiphilicity: (i) peptides with predominantly α-helix conformation at the interface and having 18-29 amino acids, and (ii) peptides with predominantly β-strand conformation at the interface and having 13-15 amino acids. In addition, high physically stable emulsions were obtained with peptides that were predicted to have axial hydrophobic/hydrophilic regions. Peptides containing the sequence FCLKVGV showed high in vitro antioxidant activity and led to emulsions with high oxidative stability. Peptide-level proteomics data and sequence analysis revealed the feasibility to obtain the potent emulsifier peptides found in this study (e.g. γ-1) by trypsin-based hydrolysis of different side streams in the potato industry.A considerable number of commercial products are oil-in-water emulsions (e.g. food, pharmaceutical, cosmetics) 1 . In addition, aqueous-based food products are enriched with hydrophobic bioactives (i.e., omega-3, vitamins A, D, E, carotenoids, flavonoids or curcumin) by using oil-in-water emulsions as delivery systems 2 . Nevertheless, oil-in-water emulsions are thermodynamically unstable systems. They tend to separate over time into their components (oil and water) due to several physical destabilization mechanisms such as creaming, flocculation, coalescence, and Ostwald ripening 3 . Emulsifiers are the most common stabilizers used in emulsions production since: (i) they facilitate emulsion formation (e.g., by reducing interfacial tension at the oil-water interface), and (ii) they provide physical stability to the emulsion (i.e., by strong steric and/or electrostatic repulsive forces) 4 . Moreover, emulsifiers also have an influence on the chemical stability of emulsions (e.g. oxidative stability) by determining the properties of the oil-water interface (i.e., thickness, porosity, charge, antioxidant activity). Indeed, these interfacial properties play a critical role on the interaction between oil and prooxidants such as radicals, oxygen and trace metals 5 .Milk proteins such as casein and whey protein are common emulsifiers used for food oil-in-water emulsions due to their excellent functional and antioxidant properties, which lead to physically and oxidatively stable emulsions 6 . Nonetheless, there is an increasing trend to replace animal proteins by plant or microbial proteins in vegetarian or vegan products, as well as to enhance food sustainability 7 . Different approaches have been suggested

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Protein hydrolysates show great promise as bioactive food and feed ingredients and for valorization of side-streams from e.g., the fish processing industry. We present a novel approach for hydrolysate characterization that utilizes proteomics data for calculation of weighted mean peptide properties (length, molecular weight, and charge) and peptide-level abundance estimation. Using a novel bioinformatic approach for subsequent prediction of biofunctional properties of identified peptides, we are able to provide an unprecedented, in-depth characterization. The study further characterizes bulk emulsifying, foaming, and in vitro antioxidative properties of enzymatic hydrolysates derived from cod frame by application of Alcalase and Neutrase, individually and sequentially, as well as the influence of heat pre-treatment. All hydrolysates displayed comparable or higher emulsifying activity and stability than sodium caseinate. Heat-treatment significantly increased stability but showed a negative effect on the activity and degree of hydrolysis. Lower degrees of hydrolysis resulted in significantly higher chelating activity, while the opposite was observed for radical scavenging activity. Combining peptide abundance with bioinformatic prediction, we identified several peptides that are likely linked to the observed differences in bulk emulsifying properties. The study highlights the prospects of applying proteomics and bioinformatics for hydrolysate characterization and in food protein science.

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