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      Identification and molecular mechanism of novel tyrosinase inhibitory peptides from collagen

      1 , 1 , 2 , 2
      Journal of Food Science
      Wiley

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          Abstract

          Abstract

          This study aimed to identify novel tyrosinase inhibitory peptides from collagen of donkey by combining in silico screening with in vitro activity verification, and to elucidate inhibition mechanism based on molecular docking and molecular dynamics simulation. Three tripeptides, that is, Asp‐Gly‐Leu (DGL), Gly‐Ala‐Arg (GAR), and Ser‐Asp‐Trp (SDW) were identified and exerted potent tyrosinase inhibitory activities, with IC 50 values of 0.47 ± 0.01 mM, 1.13 ± 0.04 mM, and 2.08 ± 0.01 mM, respectively. Each of three identified peptides had hydrophobic amino acids and could stably and closely bind with the active pocket of tyrosinase. Hydrogen bonds played the most important roles in impacting the structure stabilities of the peptide‐tyrosinase complexes. Moreover, His85, His244, His259, and Asn260 were the key residues to drive the interactions between the peptides and tyrosinase. Overall, collagen‐derived peptides DGL, GAR, and SDW from donkey had great potential as tyrosinase inhibitory peptides.

          Practical Application

          This study has suggested that three tripeptides DGL, GAR, and SDW derived from collagen of donkey have potent tyrosinase inhibitory activity. These novel collagen‐derived peptides had great potential to be applied as tyrosinase inhibitory peptides to prevent and improve hyperpigmentation disorders and other tyrosinase‐related problems in the food industry. And this work is expected to provide a theoretical basis for the development of novel, safe, and effective tyrosinase inhibitory peptides.

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          Most cited references40

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          Functional and bioactive properties of collagen and gelatin from alternative sources: A review

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            Brain tyrosinase overexpression implicates age-dependent neuromelanin production in Parkinson’s disease pathogenesis

            In Parkinson’s disease (PD) there is a selective degeneration of neuromelanin-containing neurons, especially substantia nigra dopaminergic neurons. In humans, neuromelanin accumulates with age, the latter being the main risk factor for PD. The contribution of neuromelanin to PD pathogenesis remains unknown because, unlike humans, common laboratory animals lack neuromelanin. Synthesis of peripheral melanins is mediated by tyrosinase, an enzyme also present at low levels in the brain. Here we report that overexpression of human tyrosinase in rat substantia nigra results in age-dependent production of human-like neuromelanin within nigral dopaminergic neurons, up to levels reached in elderly humans. In these animals, intracellular neuromelanin accumulation above a specific threshold is associated to an age-dependent PD phenotype, including hypokinesia, Lewy body-like formation and nigrostriatal neurodegeneration. Enhancing lysosomal proteostasis reduces intracellular neuromelanin and prevents neurodegeneration in tyrosinase-overexpressing animals. Our results suggest that intracellular neuromelanin levels may set the threshold for the initiation of PD.
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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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                Author and article information

                Journal
                Journal of Food Science
                Journal of Food Science
                Wiley
                0022-1147
                1750-3841
                June 2022
                May 23 2022
                June 2022
                : 87
                : 6
                : 2744-2756
                Affiliations
                [1 ] College of Food Science and Engineering Bohai University Jinzhou P.R. China
                [2 ] School of Food Science and Engineering Hainan University Haikou P.R. China
                Article
                10.1111/1750-3841.16160
                35603815
                71ae90c8-09b4-4df3-8a19-426f6cab940f
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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