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      Identification of the Bioavailable Peptidome of Chia Protein Hydrolysate and the In Silico Evaluation of Its Antioxidant and ACE Inhibitory Potential

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          Abstract

          The incorporation of novel, functional, and sustainable foods in human diets is increasing because of their beneficial effects and environmental-friendly nature. Chia ( Salvia hispanica L.) has proved to be a suitable source of bioactive peptides via enzymatic hydrolysis. These peptides could be responsible for modulating several physiological processes if able to reach the target organ. The bioavailable peptides contained in a hydrolysate obtained with Alcalase, as functional foods, were identified using a transwell system with Caco-2 cell culture as the absorption model. Furthermore, 20 unique peptides with a molecular weight lower than 1000 Da and the higher statistical significance of the peptide-precursor spectrum match (−10 log P) were assessed by in silico tools to suggest which peptides could be those exerting the demonstrated bioactivity. From the characterized peptides, considering the molecular features and the results obtained, the peptides AGDAHWTY, VDAHPIKAM, PNYHPNPR, and ALPPGAVHW are anticipated to be contributing to the antioxidant and/or ACE inhibitor activity of the chia protein hydrolysates.

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          Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

          The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides. We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4–20 amino acids) and one focused on long peptides ( amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure. We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive.
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            mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

            Cardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction. In this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6–7% in both benchmarking and independent datasets. The user-friendly online prediction tool, mAHTPred is freely accessible at http://thegleelab.org/mAHTPred. Supplementary data are available at Bioinformatics online.
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              The Chemical Composition and Nutritional Value of Chia Seeds—Current State of Knowledge

              Chia (Salvia hispanica) is an annual herbaceous plant, the seeds of which were consumed already thousands of years ago. Current research results indicate a high nutritive value for chia seeds and confirm their extensive health-promoting properties. Research indicates that components of chia seeds are ascribed a beneficial effect on the improvement of the blood lipid profile, through their hypotensive, hypoglycaemic, antimicrobial and immunostimulatory effects. This article provides a review of the most important information concerning the potential application of chia seeds in food production. The chemical composition of chia seeds is presented and the effect of their consumption on human health is discussed. Technological properties of chia seeds are shown and current legal regulations concerning their potential use in the food industry are presented.
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                Author and article information

                Journal
                J Agric Food Chem
                J Agric Food Chem
                jf
                jafcau
                Journal of Agricultural and Food Chemistry
                American Chemical Society
                0021-8561
                1520-5118
                02 February 2024
                14 February 2024
                : 72
                : 6
                : 3189-3199
                Affiliations
                []Department of Food and Health, Instituto de la Grasa (IG-CSIC) , Ctra. Utrera Km 1, 41013 Seville, Spain
                []Department of Medical Biochemistry, Molecular Biology, and Immunology, School of Medicine, University of Seville , Av. Sanchez Pizjuan s/n, 41009 Seville, Spain
                [§ ]Instituto de Biomedicina de Sevilla, IBiS/Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla , Av. Manuel Siurot s/n, 41013 Seville, Spain
                []Department of Cell Biology, Faculty of Biology, University of Seville , Av. Reina Mercedes s/n, 41012 Seville, Spain
                Author notes
                Author information
                https://orcid.org/0000-0003-1326-7452
                https://orcid.org/0009-0002-2902-0518
                https://orcid.org/0000-0001-5400-3192
                Article
                10.1021/acs.jafc.3c05331
                10870759
                38305180
                a9a7b6a8-697c-42e6-be4b-ef1684663ab8
                © 2024 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 31 July 2023
                : 23 January 2024
                : 17 January 2024
                Funding
                Funded by: Ministerio de Ciencia, Innovación y Universidades, doi 10.13039/100014440;
                Award ID: CYTED-2019/119RT0567
                Funded by: Ministerio de Ciencia e Innovación, doi 10.13039/501100004837;
                Award ID: TED2021-130521A-I00
                Funded by: Ministerio de Ciencia e Innovación, doi 10.13039/501100004837;
                Award ID: FJC2022-050043-I
                Funded by: European Commission, doi 10.13039/501100000780;
                Award ID: NA
                Funded by: Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía, doi 10.13039/100016970;
                Award ID: US-1381492
                Funded by: Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía, doi 10.13039/100016970;
                Award ID: P20_00661
                Categories
                Article
                Custom metadata
                jf3c05331
                jf3c05331

                Food science & Technology
                ace,bioactive peptides,dpph,identification,protease,subtilisin
                Food science & Technology
                ace, bioactive peptides, dpph, identification, protease, subtilisin

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