Brown goat yogurt: Metabolomics, peptidomics, and sensory changes during production

Supramolecular self-assembly strategies of natural-based β-lactoglobulin modulating bitter perception of goat milk-derived bioactive peptides

Complete self-assembly and reassembly behavior of bitter peptide-protein necessitates multilevel theories that encompass phenomena ranging from the self-assembly of recombinant complex to atomic trajectories. An extension to the level of mechanism method was put forth, involves limited enzymatic digestion and bottom-up proteomics to dissect inherent heterogeneity within β-LG and β-LG-PPGLPDKY complex and uncover conformational and dynamic alterations occurring in specific local regions of the model protein. Bitter peptide PPGLPDKY spontaneously bound to IIAEKTK, IDALNENK, and YLLFCMENSAEPEQSLACQCLVR regions of β-LG in a 1:1 stoichiometric ratio to mask bitterness perception. Molecular dynamic simulation and free energy calculation provided time-varying atomic trajectories of the recombinant complex and found that a peptide was stabilized in the upper region of the hydrophobic cavity with the binding free energy of -30.56 kJ mol-1 through 4 hydrogen bonds (Glu74, Glu55, Lys69, and Ser116) and hydrophobic interactions (Asn88, Asn90, and Glu112). Current research aims to provide valuable physical insights into the macroscopic self-assembly behavior between proteins and bitter peptides, and the meticulous design of highly acceptable taste characteristics in goat milk products.

Physicochemical, microbiological and metabolomics changes in yogurt supplemented with lactosucrose

Lactosucrose (LS) is a known prebiotic that has gained recognition for its low caloric content and various health benefits. However, its potential in food applications remains largely unexplored. In this study the effects of adding LS to milk at concentrations (0 %, 2 %, 5 % and 8 % w/v) for yogurt production, and the relevant changes in yogurt texture, microbial composition and metabolomics were investigated. Our findings revealed that LS played a role in promoting the formation of a structured gel during fermentation, resulting in increased elasticity and viscosity while reducing fluidity. Additionally incorporating high doses of LS into yogurt led to reduced post-acidification, enhanced survival of starter bacteria, improved water retention capacity and overall texture throughout a refrigerated storage period of 21 days. Notably higher concentrations of LS (8 % w/v) exhibited effects on enhancing yogurt quality. Furthermore, untargeted metabolomics analysis using UPLC Q TOF MS/MS revealed 45 differentially expressed metabolites, including up-regulated L-arginine, L-proline and L-glutamic acid along with the down-regulated glutathione, L-tyrosine, L-phenylalanyl and L-proline. These differential metabolites were primarily associated with amino acid metabolism such as thiamine metabolism, nicotinic acid salt and nicotinamide metabolism, and pyrimidine metabolism. As a result, the inclusion of LS in yogurt had an impact on the production of various beneficial metabolites in yogurt, highlighting the importance of combining prebiotic LS with probiotics to obtain desired physiological benefits of yogurt.

Selenium Decipher: Trapping of Native Selenomethionine-Containing Peptides in Selenium-Enriched Milk and Unveiling the Deterioration after Ultrahigh-Temperature Treatment

Selenopeptide identification relies on databases to interpret the selenopeptide spectra. A common database search strategy is to set selenium as a variable modification instead of sulfur on peptides. However, this approach generally detects only a fraction of selenopeptides. An alternative approach, termed Selenium Decipher, is proposed in the present study. It involves identifying collision-induced dissociation-cleavable selenomethionine-containing peptides by iteratively matching the masses of seleno-amino acids in selenopeptide spectra. This approach uses variable-data-independent acquisition (vDIA) for peptide detection, providing a flexible and customizable window for secondary mass spectral fragmentation. The attention mechanism was used to capture global information on peptides and determine selenomethionine-containing peptide backbones. The core structure of selenium on selenomethionine-containing peptides generates a series of fragment ions, namely, C3H7Se+, C4H10NSe+, C5H7OSe+, C5H8NOSe+, and C7H11N2O2Se+, with known mass gaps during higher-energy collisional dissociation (HCD) fragmentation. De-selenium spectra are generated by removing selenium originating from selenium replacement and then reassigning the precursors to peptides. Selenium-enriched milk is obtained by feeding selenium-rich forage fed to cattle, which leads to the formation of native selenium through biotransformation. A novel antihypertensive selenopeptide Thr-Asp-Asp-Ile-SeMet-Cys-Val-Lys TDDI(Se)MCVK was identified from selenium-enriched milk. The selenopeptide (IC50 = 60.71 μM) is bound to four active residues of the angiotensin-converting enzyme (ACE) active pocket (Ala354, Tyr523, His353, and His513) and two active residues of zinc ligand (His387 and Glu411) and exerted a competitive inhibitory effect on the spatial blocking of active sites. The integration of vDIA and the iteratively matched seleno-amino acids was applied for Selenium Decipher, which provides high validity for selenomethionine-containing peptide identification.

Systematic investigation on the multi-scale mechanisms of bitter peptide self-assembly for flavor modulation

Suppressing the aversive bitterness of bioactive peptides is an arduous task as it hinders product acceptability. Three acquisition modes (ddMS2, vDIA, and mDIA) of high-resolution mass spectrometry (HRMS) were designed for structure confirmation and accurate quantification of HPFLEWAR, with the mDIA mode chosen as optimum. HRMS and isothermal titration calorimetry was used to elucidate the mechanism that β-lactoglobulin self-assemble to form association complex in 1:1 stoichiometric ratio (ΔG value - 29.36 kJ mol-1), which automatically attracted HPFLEWAR and reduces its distribution in free form, downgraded the level of bitter perception. Proteomics experiments and molecular dynamics simulations was built to discovered that HPFLEWAR bound and stabilized in the negatively charged region of β-lactoglobulin via four hydrogen bonds (Lys69, Ile72, Asp53, and Glu74) and hydrophobic interactions. These findings were considered to give theoretical foundation for strictly controlling the bitter perception of peptides and the possible application of HPFLEWAR as new functional components.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

3-Chloropropane-1,2-diol exposure adversely influenced the bio-accessibility signatures of digested infant foods by suppressing the destabilization of α-lactalbumin and d-aspartate oxidase in a dose-dependent manner

The potential mechanisms about the health risks of endogenous 3-MCPD remain elusive. Here, we researched the influences of 3-MCPD on the metabolic landscape of digested goat infant formulas via integrative UHPLC-Q-Orbitrap HRMS-MS/MS-based peptidomics and metabolomics (%RSDs ≤ 7.35 %, LOQ 2.99-58.77 μg kg^-1). Digested goat infant formulas under 3-MCPD-interference caused metabolic perturbation by down-regulating levels of peptides VGINYWLAHK (5.98-0.72 mg kg^-1) and HLMCLSWQ (3.25-0.72 mg kg^-1) pertained to health-promoting bioactive components, and accelerated the down-regulation of non-essential amino acids (AAs, l-tyrosine 0.88-0.39 mg kg^-1, glutamic acid 8.83-0.88 μg kg^-1, and d-aspartic acid 2.93-0.43 μg kg^-1), semi-essential AA (l-arginine 13.06-8.12 μg kg^-1) and essential AAs (l-phenylalanine 0.49-0.05 mg kg^-1) that provide nutritional value. Peptidomics and metabolomics interactions elucidated that 3-MCPD altered the stability of α-lactalbumin and d-aspartate oxidase in a dose-dependent manner, and affected the flavor perception of goat infant formulas, leading to a decline of nutritional value of goat infant formulas.