Antioxidant peptides derived from potato, seaweed, microbial and spinach proteins: Oxidative stability of 5% fish oil-in-water emulsions.

In this study, we used a combination of quantitative proteomics and bioinformatic prediction for identifying novel antioxidant peptides. Thirty-five peptides from potato, seaweed, microbial, and spinach proteins were investigated. Based on high DPPH radical scavenging activity (IC50 ≤ 16 mg/mL), metal chelation activity, isoelectric point, and high relative abundance in the parent protein sources, 11 peptides were selected. Lipid oxidation retardation was evaluated in 5% fish oil-in-water emulsions stabilized with Tween 20, where emulsion physical stability was unaffected by peptide addition. The secondary structure of selected peptides was similar in the aqueous solution and emulsions, as confirmed by synchrotron radiation circular dichroism spectroscopy. The emulsions containing the selected peptides had lower levels of hydroperoxides and volatile compounds during storage compared to the control (without peptide). This study contributes to elucidating the effect of antioxidant peptides in emulsions and demonstrates the ability of quantitative proteomics and bioinformatics prediction to identify peptides with strong antioxidant properties.

Plasma proteomics data from hibernating and active Scandinavian brown bears.

In this article, we present mass-spectrometry based plasma proteomics data from hibernating and active free-ranging Scandinavian brown bears (Ursus arctos). The brown bear hibernates for half the year. Despite obesity when entering the den and the prolonged period of inactivity, the bear shows no signs of the harmful effects associated with these conditions in humans. Thus, the hibernating bear is a potential translational model for addressing these complications in humans. We analyzed plasma samples from fourteen 2- to 3-year-old bears (6 males and 8 females) collected both during hibernation and the active state, and for some of the bears during two seasons, resulting in a total of 38 analyzed plasma samples. In triplicates, the plasma proteins were unfolded and reduced. To increase the chance of detecting low-molecular-weight proteins and peptides, we filtered the samples using a 50 K molecular weight cut-off filter with the aim to deplete larger abundant proteins, including albumin, and thereby increase the depth of the analysis. The proteins in the permeate were then tryptically digested, desalted, and analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein identification and quantification was performed with the MaxQuant software searching against an Ursus arctos horribilis protein database. Here, we provide the raw data, a list with identified proteins in the plasma samples, and the databases applied for protein identification. Based on the provided data, differentially expressed proteins in hibernation compared to active state can be identified. These proteins may be involved in the bears' adaptions to hibernation physiology and hold potential as novel therapeutic targets.

Enzymatic extraction improves intracellular protein recovery from the industrial carrageenan seaweed Eucheuma denticulatum revealed by quantitative, subcellular protein profiling: A high potential source of functional food ingredients.

Seaweeds are regarded as a sustainable source of food protein, but protein extraction is severely impaired by the complex extracellular matrix. In this work, we investigated the protein-level effects of enzymatic extraction upstream of carrageenan extraction for the industrial red seaweed Eucheuma denticulatum. Combination of quantitative proteomics and bioinformatic prediction of subcellular localization was shown to have immense potential for process evaluation; even in the case of poorly annotated species such as E. denticulatum. Applying cell wall degrading enzymes markedly improved the relative recovery of intracellular proteins compared to treatment with proteolytic enzymes or no enzymatic treatment. Moreover, results suggest that proteomics data may prove useful for characterizing amino acid composition and that length-normalization is a viable approach for relative protein quantification in non-specific analysis. Importantly, the extracts were abundant in proteins, which contained both previously verified and novel, potential bioactive peptides, highlighting their potential for application as functional food ingredients.

Emulsifier peptides derived from seaweed, methanotrophic bacteria, and potato proteins identified by quantitative proteomics and bioinformatics.

Global focus on sustainability has accelerated research into alternative non-animal sources of food protein and functional food ingredients. Amphiphilic peptides represent a class of promising biomolecules to replace chemical emulsifiers in food emulsions. In contrast to traditional trial-and-error enzymatic hydrolysis, this study utilizes a bottom-up approach combining quantitative proteomics, bioinformatics prediction, and functional validation to identify novel emulsifier peptides from seaweed, methanotrophic bacteria, and potatoes. In vitro functional validation reveal that all protein sources contained embedded novel emulsifier peptides comparable to or better than sodium caseinate (CAS). Thus, peptides efficiently reduced oil-water interfacial tension and generated physically stable emulsions with higher net zeta potential and smaller droplet sizes than CAS. In silico structure modelling provided further insight on peptide structure and the link to emulsifying potential. This study clearly demonstrates the potential and broad applicability of the bottom-up approach for identification of abundant and potent emulsifier peptides.

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

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.

Biofunctionality of Enzymatically Derived Peptides from Codfish (Gadus morhua) Frame: Bulk In Vitro Properties, Quantitative Proteomics, and Bioinformatic Prediction.

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.

Identification of emulsifier potato peptides by bioinformatics: application to omega-3 delivery emulsions and release from potato industry side streams.

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 ß-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.

Peptide-Stabilized, Fluorescent Silver Nanoclusters: Solid-Phase Synthesis and Screening.

Few-atom silver nanoclusters (AgNCs) can exhibit strong fluorescence; however, they require ligands to prevent aggregation into larger nanoparticles. Fluorescent AgNCs in biopolymer scaffolds have so far mainly been synthesized in solution, and peptides have only found limited use compared to DNA. Herein, we demonstrate how solid-phase methods can increase throughput dramatically in peptide ligand screening and in initial evaluation of fluorescence intensity and chemical stability of peptide-stabilized AgNCs (P-AgNCs). 9-Fluorenylmethyloxycarbonyl (Fmoc) solid-phase peptide synthesis on a hydroxymethyl-benzoic acid (HMBA) polyethylene glycol polyacrylamide copolymer (PEGA) resin enabled on-resin screening and evaluation of a peptide library, leading to identification of novel peptide-stabilized, fluorescent AgNCs. Using systematic amino acid substitutions, we synthesized and screened a 144-member library. This allowed us to evaluate the effect of length, charge, and Cys content in peptides used as ligands for AgNC stabilization. The results of this study will enable future spectroscopic studies of these peptide-stabilized AgNCs for bioimaging and other applications.