Elsevier

LWT

Volume 130, August 2020, 109616
LWT

An in silico model to predict and estimate digestion-resistant and bioactive peptide content of dairy products: A primarily study of a time-saving and affordable method for practical research purposes

https://doi.org/10.1016/j.lwt.2020.109616Get rights and content

Highlights

  • 76 peptides with known biological properties could be identified among 226 types.

  • β-casein compared to other proteins, produces more fragments per molecule.

  • The cheese had the highest BP content, while the butter had the lowest BP content.

  • There are 6700.241 μmol digestion-resistant peptide in 100 g milk.

  • From total of milk-derived peptides, 1880.434 μmol have anti-diabetic function.

Abstract

The purpose of this study is to estimate the concentration of digestion-resistant and bioactive peptides in dairy products using an in silico method. The major contributors of milk protein sequences including αs1-casein, αs2-casein, β-casein, k-casein, β-lactoglobulin, and α-lactalbumin were obtained from UniProt Knowledgebase (UniProtKB). In silico digestion and bioactive fragment, findings were analyzed using the BIOPEP tool. Bioactive peptide content of the dairy products was estimated based on molecular weight, percent of major proteins existing in the food items, and the number of peptides obtained after in silico digestion from each protein. The results showed that 100 g milk contains 6700.241 μmol digestion-resistant peptides; in which 1880.434 μmol out of total peptides have anti-diabetic properties. Of all digestion-resistant peptides, 1978.24, 1955.024, 1700.907, and 1066.07 μmol belong to very low, low, medium, and high bioactivity sub-groups, respectively. Using the data introduced here, risk assessment could be done for dairy originated bioactive peptides and chronic disease.

Introduction

In the last few decades, natural ingredients in specific raw and processed foods have been of great interest to the different fields of science and industry (Li-Chan, 2015). These compounds have diverse biological activities and are widely employed in the food and pharmaceutical industry (Betoret, Betoret, Vidal, & Fito, 2011; Vieira da Silva, Barreira, & Oliveira, 2016). Functional foods and nutraceuticals are produced by components derived from plant or animal sources and have attracted the attention of scientists in recent decades (Nazhand et al., 2020). Nutraceuticals can play a role in preventing many diseases including heart disease, stroke, and type 2 diabetes if their mechanism of action is fully established and their safety is confirmed (Durazzo, Lucarini, & Santini, 2020; Santini, Novellino, Armini, & Ritieni, 2013). The molecules with amino acids as their building blocks such as food-derived bioactive peptides (BPs) are one of the major components of foods that affect the functional and biological activity of food products. BPs usually consist of short chains of 2–20 amino acids and become biologically active following the enzymatic or microbial hydrolysis of their parent proteins where they are encrypted (Mohanty, Mohapatra, Misra, & Sahu, 2016). The function of proteolytic enzymes is very important in the synthesis of BPs during gastrointestinal digestion or food processing such as ripening and fermentation by breaking proteins into shorter fragments (Toldrá, Reig, Aristoy, & Mora, 2018).

Several food products have been analyzed for their potential BP content, but, milk and dairy products are one of the richest sources of BPs and many studies have been published about the functional properties of dairy product-derived BPs (Basilicata et al., 2018; Georgalaki et al., 2017). These BPs possess very essential biological activities and functionalities including antimicrobial, antihypertensive, antioxidative, anticytotoxic, immunomodulatory, opioid, and mineral-carrying activities (Korhonen & Pihlanto–Leppälä, 2016; Lopez-Exposito & Recio, 2008; Mada, Ugwu, & Abarshi, 2019). Numerous studies have shown that α-, β-, and κ-casein (Bessette et al., 2016) and whey proteins such as α-lactalbumin, β-lactoglobulin, and glycomacropeptide (GMP) (Arrutia, Rubio, & Riera, 2016) are prominent parent proteins which are major sources for releasing a number of BPs. Despite their promising benefits, unfortunately, scale-up production of BPs is mainly hindered by bioprocess challenges related to purification and quantification (Agyei, Ongkudon, Wei, Chan, & Danquah, 2016).

An efficient quantification of the beneficial effects of BPs is highly required to better understand their role in our health. Although in vitro (Kwon et al., 2011), in vivo (Carrizzo et al., 2019) and meta-analysis of randomized control trial (Fekete, Givens, & Lovegrove, 2015) studies have been done to explore the short-term beneficial effects of BPs (especially dairy originated ones) on human health, investigations on the long-term beneficial effects of these BPs are still at unsatisfactory levels. This is mostly due to the general lack of consistent methods to better understand their synthesis from different food products (Chakrabarti, Guha, & Majumder, 2018). Cohort and case-control studies are commonly used to assess the long-term effects of food-ingredients (Song & Chung, 2010). To the best of our knowledge, due to the lack of quantitative measurement of BPs in food items, no case-control or cohort studies have been done to reveal the association of BPs intake and reduction in chronic diseases. The major reason why BPs content of food items have not been measured so far is the high cost of BPs measurement and characterization. Therefore, if we successfully measure the BP content of food items included in food frequency questionnaires (FFQ) used in cohort or case-control studies, the association between BPs intake and their effect on chronic diseases such as cancer, cardiovascular disease, and other diseases can be effectively assessed.

The rapid promotion of bioinformatics leads to the establishment of integrated biological knowledge databases such as PepBank, BioPD, BIOPEP, EROP-Moscow, and SwePep. Among all, BIOPEP focuses mainly on peptides of food origins (Iwaniak, Minkiewicz, Darewicz, Sieniawski, & Starowicz, 2016; Kęska, Stadnik, Kononiuk, Libera, & Wójciak, 2018). In the food sciences, computer simulation (in silico) can be used as a rapid and low-cost primary screening tool for sequencing bioactive peptides analysis and their biological effects in various foods (Sayd et al., 2018). In silico studies allow the creation of profiles for potential biological activities of proteins, as well as all activities which may be found in a protein sequence (Carrasco-Castilla, Hernández-Álvarez, Jiménez-Martínez, Gutiérrez-López, & Dávila-Ortiz, 2012). Also, in silico proteolysis can help us to select the appropriate enzyme for hydrolysis in the simulation of gastrointestinal digestion and is useful for identifying the relationship between peptide structure and its function (Lin et al., 2018).

Research on BPs has shown that they have great potential to improve human health and prevent chronic diseases by exerting positive effects on the body's digestive, cardiovascular, immune, and nervous systems, but the beneficial effects of them should also be proved in long term studies. In vitro and/or in vivo measurement and characterization of dairy originated BPs are time-consuming and expensive. Therefore, the objective of the present study is to estimate the amount of BPs in dairy products using an in silico method.

Section snippets

Bovine milk proteins sequences

According to the literature, the initial sequence of six types of food protein derived from cow's milk was used in this study. The major contributors of milk protein sequences are αs1-casein (UniProtKB - P02662), αs2-casein (UniProtKB - P02663), β-casein (UniProtKB - P02666), k-casein (UniProtKB - P02668), β-lactoglobulin (UniProtKB - P02754) and α-lactalbumin (UniProtKB - P00711) obtained from UniProt Knowledgebase (UniProtKB) (https://www.uniprot.org) and re-checked in The National Center for

Results

In the present study, six different proteins derived from dairy products were included for in situ analysis. The mass of the pre-proteins, signal peptides, and mature proteins are reported in Table 2. Among the proteins, αs2-casein (Mass: 24,331.07 Da) and α-lactalbumin (Mass: 14,168.88 Da) have the highest and lowest masses, respectively. As reported in Table 3, after in silico digestion of each molecule of αs1-casein, αs2-casein, β-casein, k-casein, α-Lactalbumin, and β-lactoglobulin, the

Discussion

The digestion-resistant and bioactive peptide content of yogurt, low-fat yogurt, high-fat yogurt, Doogh, cheese, cream cheese, ice cream (industrial), ice cream (traditional), butter, cream and kashk (kashk as a fermented dairy product is produced either in dry or liquid form) were estimated based on MW, percent of major proteins existing in the food items and a number of peptides obtained after in silico digestion from each protein. The selection of proteolytic enzymes is also essential for a

Conclusion

In this in silico study, we reported the amount of BPs content of dairy products from FFQ used in the TLGS, a large-scale community-based prospective study. Using the results obtained from the current study risk assessment could be performed on whether dietary intake of digestion-resistant peptides decreases the risk of chronic diseases such as CVD and cancer or not. The validity of the data reported in this study should be evaluated by in vitro (enzymatic digestion) and in vivo (stability of

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Conceptualization, M.B; Data collection and analysis, M.B, F.J, M.J; writing—original draft preparation, A.M.Y, FF, F.J, M.B.; Software, M.B, H.F; review and editing, A.M.K and I.E, S.H.D. All authors have read and agreed to the published version of the manuscript.

CRediT authorship contribution statement

Meisam Barati: Software, Writing - original draft, Formal analysis, Data curation, Conceptualization. Fardin Javanmardi: Writing - original draft, Formal analysis, Data curation. Masoumeh Jabbari: Formal analysis, Data curation, Resources, Visualization. Amin Mokari-Yamchi: Writing - original draft, Methodology, Validation. Fariba Farahmand: Investigation, Resources. Ismail Eş: Writing - review & editing. Hossein Farhadnejad: Software. Sayed Hossein Davoodi: Project administration, Funding

Declaration of competing interest

The authors declare that they have no conflict of interest.

Acknowledgements

This study is related to the project NO 20803 with a code of ethics “IR.SBMU.RETECH.REC.1398.774” from the Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran. We also appreciate the “Student Research Committee” and “Research & Technology Chancellor” at Shahid Beheshti University of Medical Sciences for their financial support of this study.

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