FeptideDB: A web application for new bioactive peptides from food protein

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

Functional attributes of bioactive peptides of bovine milk origin and application of in silico approaches for peptide prediction and functional annotations

Bovine milk peptides are the protein fragments with diverse bioactive properties having antioxidant, anticarcinogenic, other therapeutic and nutraceutical potentials. These peptides are formed in milk by enzymatic hydrolysis, gastrointestinal digestion and fermentation processes. They have significant health impact with high potency and low toxicity making them a suitable natural alternative for preventing and managing diseases. Antibiotic resistance has increased the quest for better peptide candidates with antimicrobial effects. This article presents a comprehensive review on well documented antimicrobial, immunological, opioid, and anti-hypertensive activities of bovine milk peptides. It also covers the usage of computational biology tools and databases for prediction and analysis of the food-derived bioactive peptides. In silico analysis of amino acid sequences of Bos taurus milk proteins have been predicted to generate peptides with dipeptidyl peptidase IV inhibitory and ACE inhibitory properties, making them favorable candidates for developing blood sugar lowering drugs and anti-hypertensives. In addition to the prediction of new bioactive peptides, application of bioinformatics tools to predict novel functions of already known peptides is also discussed. Overall, this review focuses on the reported as well as predicted biologically active peptide of casein and whey proteins of bovine milk that can be utilized to develop therapeutic agents.

Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

Screening, separation and identification of metal-chelating peptides for nutritional, cosmetics and pharmaceutical applications

Metal-chelating peptides, which form metal-peptide coordination complexes with various metal ions, can be used as biofunctional ingredients notably to enhance human health and prevent diseases. This review aims to discuss recent insights into food-derived metal-chelating peptides, the strategies set up for their discovery, their study, and identification. After understanding the overall properties of metal-chelating peptides, their production from food-derived protein sources and their potential applications will be discussed, particularly in nutritional, cosmetics and pharmaceutical fields. In addition, the review provides an overview of the last decades of progress in discovering food-derived metal-chelating peptides, addressing several screening, separation and identification methodologies. Furthermore, it emphasizes the methods used to assess peptide-metal interaction, allowing for better understanding of chemical and thermodynamic parameters associated with the formation of peptide-metal coordination complexes, as well as the specific amino acid residues that play important roles in the metal ion coordination.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Recent advances in the exploration and discovery of SARS-CoV-2 inhibitory peptides from edible animal proteins

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), is spreading worldwide. Although the COVID-19 epidemic has passed its peak of transmission, the harm it has caused deserves our attention. Scientists are striving to develop medications that can effectively treat COVID-19 symptoms without causing any adverse reactions. SARS-CoV-2 inhibitory peptides derived from animal proteins have a wide range of functional activities in addition to safety. Identifying animal protein sources is crucial to obtaining SARS-CoV-2 inhibitory peptides from animal sources. This review aims to reveal the mechanisms of action of these peptides on SARS-CoV-2 and the possibility of animal proteins as a material source of SARS-CoV-2 inhibitory peptides. Also, it introduces the utilization of computer-aided design methods, phage display, and drug delivery strategies in the research on SARS-CoV-2 inhibitor peptides from animal proteins. In order to identify new antiviral peptides and boost their efficiency, we recommend investigating the interaction between SARS-CoV-2 inhibitory peptides from animal protein sources and non-structural proteins (Nsps) using a variety of technologies, including computer-aided drug approaches, phage display techniques, and drug delivery techniques. This article provides useful information for the development of novel anti-COVID-19 drugs.

Advances in separation and identification of biologically important milk proteins and peptides

Milk is a rich source of biologically important proteins and peptides. In addition, milk contains a variety of extracellular vesicles (EVs), including exosomes, that carry their own proteome cargo. EVs are essential for cell-cell communication and modulation of biological processes. They act as nature carriers of bioactive proteins/peptides in targeted delivery during various physiological and pathological conditions. Identification of the proteins and protein-derived peptides in milk and EVs and recognition of their biological activities and functions had a tremendous impact on food industry, medicine research, and clinical applications. Advanced separation methods, mass spectrometry (MS)-based proteomic approaches and innovative biostatistical procedures allowed for characterization of milk protein isoforms, genetic/splice variants, posttranslational modifications and their key roles, and contributed to novel discoveries. This review article discusses recently published developments in separation and identification of bioactive proteins/peptides from milk and milk EVs, including MS-based proteomic approaches.

Hmrbase2: a comprehensive database of hormones and their receptors

PURPOSE

Hormones play a critical role in regulating various physiological processes and any hormonal imbalances can lead to major endocrine disorders. Thus, studying hormones is essential for both the therapeutics and the diagnostics of hormonal diseases. To facilitate this need, we have developed Hmrbase2, a comprehensive platform that provides extensive information on hormones.

METHODS

Hmrbase2 is a web-based database which is an update of a previously published database, Hmrbase ( http://crdd.osdd.net/raghava/hmrbase/ ). We collected a large amount of information on peptide and non-peptide hormones and hormone receptors, this information being sourced from Hmrbase, HMDB, UniProt, HORDB, ENDONET, PubChem, and the medical literature.

RESULTS

Hmrbase2 contains a total of 12,056 entries, which is more than twice the number of entries contained in the previous version Hmrbase. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones, and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties such as melting point and water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been supplied. Additionally, a similarity search module has been incorporated enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman.

CONCLUSIONS

To make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Hmrbase2 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2 .

Identification of Potential Bioactive Peptides in Sheep Milk Kefir through Peptidomic Analysis at Different Fermentation Times

Sheep farming is an important socioeconomic activity in most Mediterranean countries, particularly Spain, where it contributes added value to rural areas. Sheep milk is used in Spain mainly for making cheese, but it can be used also for making other dairy products, such as the lactic-alcoholic fermentation product known as kefir. Dairy products have health benefits because, among other reasons, they contain molecules with biological activity. In this work, we performed a proteomics strategy to identify the peptidome, i.e., the set of peptides contained in sheep milk kefir fermented for four different periods of time, aiming to understand changes in the pattern of digestion of milk proteins, as well as to identify potential bioactive peptides. In total, we identified 1942 peptides coming from 11 different proteins, and found that the unique peptides differed qualitatively among samples and their numbers increased along the fermentation time. These changes were supported by the increase in ethanol, lactic acid, and D-galactose concentrations, as well as proteolytic activity, as the fermentation progressed. By searching in databases, we found that 78 of the identified peptides, all belonging to caseins, had potential biological activity. Of these, 62 were not previously found in any milk kefir from other animal species. This is the first peptidomic study of sheep milk kefir comprising time-course comparison.

A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides

Food protein-derived antihypertensive peptides are a representative type of bioactive peptides. Several models based on partial least squares regression have been constructed to delineate the relationship between the structure and activity of the peptides. Machine-learning-based models have been applied in broad areas, which also indicates their potential to be incorporated into the field of bioactive peptides. In this study, a long short-term memory (LSTM) algorithm-based deep learning model was constructed, which could predict the IC₅₀ value of the peptide in inhibiting ACE activity. In addition to the test dataset, the model was also validated using randomly synthesized peptides. The LSTM-based model constructed in this study provides an efficient and simplified method for screening antihypertensive peptides from food proteins.

Milk-Derived Antimicrobial Peptides: Overview, Applications, and Future Perspectives

The growing consumer awareness towards healthy and safe food has reformed food processing strategies. Nowadays, food processors are aiming at natural, effective, safe, and low-cost substitutes for enhancing the shelf life of food products. Milk, besides being a rich source of nutrition for infants and adults, serves as a readily available source of precious functional peptides. Due to the existence of high genetic variability in milk proteins, there is a great possibility to get bioactive peptides with varied properties. Among other bioactive agents, milk-originated antimicrobial peptides (AMPs) are gaining interest as attractive and safe additive conferring extended shelf life to minimally processed foods. These peptides display broad-spectrum antagonistic activity against bacteria, fungi, viruses, and protozoans. Microbial proteolytic activity, extracellular peptidases, food-grade enzymes, and recombinant DNA technology application are among few strategies to tailor specific peptides from milk and enhance their production. These bioprotective agents have a promising future in addressing the global concern of food safety along with the possibility to be incorporated into the food matrix without compromising overall consumer acceptance. Additionally, in conformity to the current consumer demands, these AMPs also possess functional properties needed for value addition. This review attempts to present the basic properties, synthesis approaches, action mechanism, current status, and prospects of antimicrobial peptide application in food, dairy, and pharma industry along with their role in ensuring the safety and health of consumers.

An integrated approach to the analysis of antioxidative peptides derived from Gouda cheese with a modified β-casein content

This study is the first to present an integrated approach involving in silico and in vitro protocols that was pursued to analyse an antioxidative potency of Gouda cheese with modified content of β-casein. Firstly, the predictions of the presence of antioxidant peptides in the casein sequences were computed using the BIOPEP-UWM database. Then, the antioxidative bioactivity of six variants of Gouda cheese (with reduced, normative, and increased content of β-casein at the initial and final stage of ripening) was assessed. Finally, the RP-HPLC–MS/MS was applied to identify antioxidative peptides in Gouda-derived water-soluble extracts (WSEs). Analyses were supported with the heatmaps and the computation of parameters describing the efficiency of proteolysis of caseins in the modified Gouda cheeses, i.e., the frequency and the relative frequency of the release of antioxidative fragments during cheese ripening (A_Eexp and W_exp., respectively). All Gouda cheese variants exhibited the antioxidative potential which differed depending on the assay employed. The highest antioxidative activity (ABTS^·+ radical scavenging effect, FRAP, and Fe-chelating) was observed for WSEs derived from Gouda cheese with increased content of β-casein after the 60th day of ripening. The results obtained suggest the potential of Gouda cheese as the antioxidant-promoting food.

BIOPEP-UWM Virtual—A Novel Database of Food-Derived Peptides with In Silico-Predicted Biological Activity

The novel BIOPEP-UWM Virtual database is designed as a repository of peptide sequences whose bioactivity or taste information was the result of in silico predictions. It is a tool complementary to the existing BIOPEP-UWM database summarizing the results of experimental data on bioactive peptides. The layout and organization of the new database are identical to those of the existing BIOPEP-UWM database of bioactive peptides. The peptide data record includes the following information: name; sequence and function information (understood as information about the predicted target biomacromolecule); bibliographic data with the reference paper describing the peptide; additional information, including the peptide structure, annotated using chemical codes as well as the specification of the method used for bioactivity prediction; information about other activities discovered experimentally or predicted using computational methods; peptide taste (if available); and a database reference tab providing information about compound annotations in other databases (if available).

Multifunctional Analysis of Chia Seed (Salvia hispanica L.) Bioactive Peptides Using Peptidomics and Molecular Dynamics Simulations Approaches

Chia seed peptides (CSP) can be a source of multifunctional biopeptides to treat non-communicable diseases. However, interactions and binding affinity involved in targeting specific receptors remains unexplored. In this study, molecular simulation techniques were used as virtual screening of CSP to determine drug-like candidates using a multi-target-directed ligand approach. CSP fraction with the best bioactivities in vitro was sequenced. Then, a prediction model was built using physicochemical descriptors (hydrophobicity, hydrophilicity, intestinal stability, antiangiogenic, antihypertensive, and anti-inflammatory) to calculate potential scores and rank possible biopeptides. Furthermore, molecular dynamics simulations (MDS) and ensemble molecular docking analysis were carried out using four human protein targets (ACE, angiotensin converting enzyme; VEGF, vascular endothelial growth factor; GLUC, glucocorticoid and MINC, mineralocorticoid receptors). Five known-sequence peptides (NNVFYPF, FNIVFPG, SRPWPIDY, QLQRWFR, GSRFDWTR) and five de novo peptides (DFKF, DLRF, FKAF, FRSF, QFRF) had the lowest energy score and higher affinity for ACE and VEGF. The therapeutic effects of these selected peptides can be related to the inhibition of the enzymes involved in angiogenesis and hypertension, due to formation of stable complexes with VEGF and ACE binding sites, respectively. The application of MDS is a good resource for identifying bioactive peptides for future experimental validation.

Conventional and in silico approaches to select promising food-derived bioactive peptides: A review

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Seaweed and edible insects are considered new sources of bioactive peptides.

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Conventional approaches are necessary to validate the bioactivity of peptides.

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Bioinformatics tools accelerate the obtaining of bioactive peptides.

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The integrated approach is a promising strategy to obtain bioactive peptides.

The interest for food-derived bioactive peptides, either from common or unconventional sources, has increased due to their potential therapeutic effect against a wide range of diseases. The study of such bioactive peptides using conventional methods is a long journey, expensive and time-consuming. Hence, bioinformatic approaches, which can not only help to predict the formation of bioactive peptides from any known protein source, but also to analyze the protein structure/function relationship, have gained a new meaning in this scientific field. Therefore, this review aims to provides an overview of conventional characterization methods and the most recent advances in the field of in silico approaches for predicting and screening promising food-derived bioactive peptides.

Potential Biological Activities of Peptides Generated during Casein Proteolysis by Curly Kale (Brassica oleracea L. var. sabellica L.) Leaf Extract: An In Silico Preliminary Study

This study is a brief report on the proteolytic activity of curly kale leaf extract against casein. Casein degradation products and an in silico analysis of the biological activity of the peptides obtained was performed. The efficiency of casein hydrolysis by curly kale extract was determined using SDS-PAGE and by peptide concentration determination. The pattern of the enzymatic activity was determined by MALDI-TOF MS analysis. The results showed that α- and β-casein were more resistant to curly kale extract hydrolysis, whereas κ-casein was absent in the protein profile after 8 h of proteolysis, and all casein fractions were completely hydrolyzed after 24 h of incubation. Based on sequence analysis, seven peptides were identified, with molecular mass in the range of 1151-3024 Da. All the peptides were products of β-casein hydrolysis. The identified amino acid sequences were analyzed in BIOPEP, MBPDB, and FeptideDB databases in order to detect the potential activities of the peptides. In silico analysis suggests that the β-casein-derived peptides possess sequences of peptides with ACE inhibitory, antioxidant, dipeptidyl peptidase IV inhibitory, antithrombotic, immunomodulatory, and antiamnesic bioactivity. Our study was first to evaluate the possibility of applying curly kale leaf extract to generate biopeptides through β-casein hydrolysis.

Viral 3CLpro as a Target for Antiviral Intervention Using Milk-Derived Bioactive Peptides

Viruses of the picornavirus-like supercluster mainly achieve cleavage of polyproteins into mature proteins through viral 3-chymotrypsin proteases (3C^pro) or 3-chymotrypsin-like proteases (3CL^pro). Due to the essential role in processing viral polyproteins, 3C^pro/3CL^pro is a drug target for treating viral infections. The 3CL^pro is considered the main protease (M^pro) of coronaviruses. In the current study, the SARS-CoV-2 M^pro inhibitory activity of di- and tri-peptides (DTPs) resulted from the proteolysis of bovine milk proteins was evaluated. A set of 326 DTPs were obtained from virtual digestion of bovine milk major proteins. The resulted DTPs were screened using molecular docking. Twenty peptides (P1–P20) showed the best binding energies (ΔG_b <  − 7.0 kcal/mol). Among these 20 peptides, the top five ligands, namely P1 (RVY), P3 (QSW), P17 (DAY), P18 (QSA), and P20 (RNA), based on the highest binding affinity and the highest number of interactions with residues in the active site of M^pro were selected for further characterization by ADME/Tox analyses. For further validation of our results, molecular dynamics simulation was carried out for P3 as one of the most favorable candidates for up to 100 ns. In comparison to N3, a peptidomimetic control inhibitor, high stability was observed as supported by the calculated binding energy of the M^pro-P3 complex (− 59.48 ± 4.87 kcal/mol). Strong interactions between P3 and the M^pro active site, including four major hydrogen bonds to HIS41, ASN142, GLU166, GLN189 residues, and many hydrophobic interactions from which the interaction with CYS145 as a catalytic residue is worth mentioning. Conclusively, milk-derived bioactive peptides, especially the top five selected peptides P1, P3, P17, P18, and P20, show promise as an antiviral lead compound.

Partial-, Double-Enzymatic Dephosphorylation and EndoGluC Hydrolysis as an Original Approach to Enhancing Identification of Casein Phosphopeptides (CPPs) by Mass Spectrometry

The identification of phosphopeptides is currently a challenge when they are part of a complex matrix of peptides, such as a milk protein enzymatic hydrolysate. This challenge increases with both the number of phosphorylation sites on the phosphopeptides and their amino acid length. Here, this paper reports a four-phase strategy from an enzymatic casein hydrolysate before a mass spectrometry analysis in order to enhance the identification of phosphopeptides and phosphosites: (i) the control protein hydrolysate, (ii) a two-step enzymatic dephosphorylation of the latter, allowing for the almost total dephosphorylation of peptides, (iii) a one-step enzymatic dephosphorylation, allowing for the partial dephosphorylation of the peptides and (iv) an additional endoGluC enzymatic hydrolysis, allowing for the cleavage of long-size peptides into shorter ones. The reverse-phase high-pressure liquid chromatography–tandem mass spectrometry (RP-HPLC-MS/MS) analyses of hydrolysates that underwent this four-phase strategy allowed for the identification of 28 phosphorylation sites (90%) out of the 31 referenced in UniprotKB/Swiss-Prot (1 June 2021), compared to 17 sites (54%) without the latter. The alpha-S2 casein phosphosites, referenced by their similarity in the UniProt database, were experimentally identified, whereas pSer148, pThr166 and pSer187 from a multiphosphorylated long-size kappa-casein were not. Data are available via ProteomeXchange with identifier PXD027132.

Schistocins: Novel antimicrobial peptides encrypted in the Schistosoma mansoni Kunitz Inhibitor SmKI-1

BACKGROUND

Here we describe a new class of cryptides (peptides encrypted within a larger protein) with antimicrobial properties, named schistocins, derived from SmKI-1, a key protein in Shistosoma mansoni survival. This is a multi-functional protein with biotechnological potential usage as a therapeutic molecule in inflammatory diseases and to control schistosomiasis.

METHODS

We used our algorithm enCrypted, to perform an in silico proteolysis of SmKI-1 and a screening for potential antimicrobial activity. The selected peptides were chemically synthesized, tested in vitro and evaluated by both structural (CD, NMR) and biophysical (ITC) studies to access their structure-function relationship.

RESULTS

EnCrypted was capable of predicting AMPs in SmKI-1. Our biophysical analyses described a membrane-induced conformational change from random coil-to-α-helix and a peptide-membrane equilibrium for all schistocins. Our structural data allowed us to suggest a well-known mode of peptide-membrane interaction in which electrostatic attraction between the cationic peptides and anionic membranes results in the bilayer disordering. Moreover, the NMR exchange H/D data with the higher entropic contribution observed for the peptide-membrane interaction showed that shistocins have different orientations upon the membrane.

CONCLUSIONS

This work demonstrate the robustness for using the physicochemical features of predicted peptides in the identification of new bioactive cryptides besides the relevance of combining these analyses with biophysical methods to understand the peptide-membrane affinity and improve further algorithms.

GENERAL SIGNIFICANCE

Bioprospecting cryptides can be conducted through data mining of protein databases demonstrating the success of our strategy. The peptides-based agents derived from SmKI-1 might have high impact for system-biology and biotechnology.

Dairy-originated digestion-resistant and bioactive peptides increase the risk of hypertension: Tehran Lipid and Glucose Study

Milk-protein-derived bioactive peptides (BPs) have been proposed as modulators of different regulatory processes involved in blood pressure regulation. Studies on the long-term effects of BPs on blood pressure have not yet been conducted. We aimed to investigate the association of dairy-originated BPs with the risk of hypertension (HTN) in the Tehran Lipid and Glucose Cohort Study (TLGS). In this cohort study, 4378 subjects with a mean follow-up period of 3.1 years were included in the final analysis. Dietary intake, physical activity, demographic, and anthropometric data and blood pressure measurements were obtained for all participants. Various types of dairy-originated BPs were determined by an in silico method. High intake of total digestion-resistant and bioactive peptides (OR: 1.31, CI 95%: 1.01-1.70), dipeptides (OR: 1.33, CI 95%: 1.03-1.73), peptides with more than seven residues (OR: 1.32, CI 95%: 1.01-1.71), glycosylated residues (OR: 1.39, CI 95%: 1.07-1.80), highly hydrophilic peptides (OR: 1.32, CI 95%: 1.01-1.71), and low hydrophobic peptides (OR: 1.32, CI 95%: 1.01-1.71) was associated with an increased risk of HTN in the adjusted model. In addition, subjects in the higher tertile of anti-HTN peptide (OR: 1.33, CI 95%: 1.02-1.72) and antidiabetic peptide (OR: 1.35, CI 95%: 1.04-1.76) intake had a higher risk of HTN than those in the lower tertile. No significant association emerged between calcium intake from dairy and incident risk of HTN. Our results showed that the intake of some forms of digestion-resistant and BPs, such as anti-HTN peptides, dipeptides, and peptides with more than seven residues, can increase the risk of HTN in the TLGS population.

FermFooDb: A database of bioactive peptides derived from fermented foods

Globally fermented foods are in demands due to their functional and nutritional benefits. These foods are sources of probiotic organisms and bioactive peptides, various amino acids, enzymes etc. that provides numerous health benefits. FermFooDb (https://webs.iiitd.edu.in/raghava/fermfoodb/) is a manually curated database of bioactive peptides derived from wide range of foods that maintain comprehensive information about peptides and process of fermentation. This database comprises of 2205 entries with following major fields, peptide sequence, Mass and IC50, food source, functional activity, fermentation conditions, starter culture, testing conditions of sequences in vitro or in vivo, type of model and method of analysis. The bioactive peptides in our database have wide range of therapeutic potentials that includes antihypertensive, ACE-inhibitory, antioxidant, antimicrobial, immunomodulatory and cholesterol lowering peptides. These bioactive peptides were derived from different types of fermented foods that include milk, cheese, yogurt, wheat and rice. Numerous, web-based tools have been integrated to retrieve data, peptide mapping of proteins, similarity search and multiple-sequence alignment. This database will be useful for the food industry and researchers to explore full therapeutic potential of fermented foods from specific cultures.

Shotgun Proteomics and Protein-Based Bioinformatics for the Characterization of Food-Derived Bioactive Peptides

A workflow for the characterization of food-derived bioactive peptides is described in this chapter. The workflow integrates two consecutive steps: a discovery phase and a protein-based bioinformatic phase. In the first step (discovery phase), a shotgun bottom-up proteomics approach is used to create a reference data set for a selected food proteome. Afterward, in a second step (bioinformatic phase), the reference proteome is subjected to several in silico protein-based bioinformatic analyses to predict and characterize potential bioactive peptides after an in silico human gastrointestinal digestion. Using this workflow, bioactive collagen peptides, antihypertensive, antimicrobial, and antitumor peptides were predicted as potential valuable bioactive peptides from seafood and marine by-products. It is concluded that the combination of the global shotgun proteomic analysis and the analysis by protein-based bioinformatics can provide a rapid strategy for the characterization of new potential food-derived bioactive peptides.

A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.

Comprehensive identification of native medium-sized and short bioactive peptides in sea bass muscle

Native peptides from sea bass muscle were analyzed by two different approaches: medium-sized peptides by peptidomics analysis, whereas short peptides by suspect screening analysis employing an inclusion list of exact m/z values of all possible amino acid combinations (from 2 up to 4). The method was also extended to common post-translational modifications potentially interesting in food analysis, as well as non-proteolytic aminoacyl derivatives, which are well-known taste-active building blocks in pseudo-peptides. The medium-sized peptides were identified by de novo and combination of de novo and spectra matching to a protein sequence database, with up to 4077 peptides (2725 modified) identified by database search and 2665 peptides (223 modified) identified by de novo only; 102 short peptide sequences were identified (with 12 modified ones), and most of them had multiple reported bioactivities. The method can be extended to any peptide mixture, either endogenous or by protein hydrolysis, from other food matrices.

BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities

The BIOPEP-UWM™ database of bioactive peptides (formerly BIOPEP) has recently become a popular tool in the research on bioactive peptides, especially on these derived from foods and being constituents of diets that prevent development of chronic diseases. The database is continuously updated and modified. The addition of new peptides and the introduction of new information about the existing ones (e.g., chemical codes and references to other databases) is in progress. New opportunities include the possibility of annotating peptides containing D-enantiomers of amino acids, batch processing option, converting amino acid sequences into SMILES code, new quantitative parameters characterizing the presence of bioactive fragments in protein sequences, and finding proteinases that release particular peptides.

Presence of Exopeptidase-Resistant and Susceptible Peptides in a Bacterial Protease Digest of Corn Gluten

Corn gluten hydrolysate (CGH) was prepared by food-grade bacterial proteases, alcalase and neutral protease. Digestion of CGH with carboxypeptidase A and leucine aminopeptidase extensively changed the elution patterns of peptides as observed from reversed phase high performance liquid chromatography-mass spectrometry (LC-MS), whereas digestion with pepsin and trypsin hardly affected the elution patterns. Twenty-five major peptides in CGH were identified. After digestion with exopeptidases, only prolyl dipeptides and pyroglutamyl di- and tripeptides remained, whereas the other 17 peptides completely disappeared. On the other hand, all 25 peptides remained after digestion with pepsin and trypsin. These facts suggest that a majority of short-chain peptides in food protein hydrolysates are degraded by exopeptidases during digestion and absorption processes. Thus, susceptibility to exopeptidases should be considered for prediction of bioactive peptide upon ingestion, which has not been considered in most of previous studies on food-derived bioactive peptides.