Complex pectin metabolism by Lactobacillus and Streptococcus suggests an effective control approach for Maillard harmful products in brown fermented milk

Pyro-Thermolysis Pattern Analysis of Selenopeptide in Selenium-Enriched Rice Based on Two-Dimensional Dietary Kinetics Evolution

Selenopeptides can be ideal dietary selenium (Se) supplements for humans. Currently, rice is not used much as a source of selenopeptides. Here, we executed the selenopeptidomics analysis of selenium-enriched rice protein hydrolysates using the full MS-dd-MS2 acquisition method and identified selenopeptides, including L{Se-Met}AK and other selenopeptides. Specifically, selenomethionine (SeMet) replaced methionine (Met) in the rice protein-Oryzain alpha chain (EC: 3.4.22) and generated a selenopeptide L{Se-Met}AK (molecular formula: C20H38N5O5Se) during subsequent protein hydrolysis. This selenopeptide was in 425-428 amino acid residues of the Oryzain alpha chain. Thermal processing led to selenopeptide cleavage, which affected the efficient retention of selenopeptides. Activation energy (Ea) was used to locate the quality control markers in the thermal degradation of selenopeptides. Therefore, this study established the thermal degradation rate equation for the selenopeptide L{Se-Met}AK at 100 °C, 110 and 120 °C; and identified the pyrolysis products, including L{Se-Met}A, LMA, LMAK, K1, and K2, involving C-N cleavage on the amide bond of alanine and lysine, C-Se bond cleavage and C-N cleavage on the amide bond of alanine and Met; the fit coefficients of the thermal reaction models were ≥0.9248, which could accurately quantify the real-time pyrolysis kinetic process; and LMAK had a lower Ea of 88.20 kJ/mol, which made it easier to produce. In summary, LMAK can be used as a quality control marker in the pyrolysis process, providing technical support for the efficient retention of selenopeptides.

Advanced functional materials as reliable tools for capturing food-derived peptides to optimize the peptidomics pre-treatment enrichment workflow

Peptidomics strategies with high throughput, sensitivity, and reproducibility are key tools for comprehensively analyzing peptide composition and potential functional activities in foods. Nevertheless, complex signal interference, limited ionization efficiency, and low abundance have impeded food-derived peptides' progress in food detection and analysis. As a result, novel functional materials have been born at the right moment that could eliminate interference and perform efficient enrichment. Of note, few studies have focused on developing peptide enrichment materials for food sample analysis. This work summarizes the development of endogenous peptide, phosphopeptide, and glycopeptide enrichment utilizing materials that have been employed extensively recently: organic framework materials, carbon-based nanomaterials, bio-based materials, magnetic materials, and molecularly imprinted polymers. It focuses on the limitations, potential solutions, and future prospects for application in food peptidomics of various advanced functional materials. The size-exclusion effect of adjustable aperture and the modification of magnetic material enhanced the sensitivity and selectivity of endogenous peptide enrichment and aided in streamlining the enrichment process and cutting down on enrichment time. Not only that, the immobilization of metal ions such as Ti4+ and Nb5+ enhanced the capture of phosphopeptides, and the introduction of hydrophilic groups such as arginine, L-cysteine, and glutathione into bio-based materials effectively optimized the hydrophilic enrichment of glycopeptides. Although a portion of the carefully constructed functional materials currently only exhibit promising applications in the field of peptide enrichment for analytical chemistry, there is reason to believe that they will further advance the field of food peptidomics through improved pre-treatment steps.

Dissecting the binding effect of Crocetin glucosyltransferase 2 in crocetin biotransformation in saffron (Crocus sativus L.) from different origins

Crocus sativus L. is a both medicinal and food bulbous flower whose qualities are geographically characterized. However, identification involving different places of origin of such substances is currently limited to single-omics mediated content analysis. Integrated metabolomics and proteomics, 840 saffron samples from six countries (Spain, Greece, Iran, China, Japan, and India) were analyzed using the QuEChERS extraction method. A total of 77 differential metabolites and 14 differential proteins were identified. The limits of detection of the method were 1.33 to 8.33 μg kg-1, and the recoveries were 85.56% to 105.18%. Using homology modeling and molecular docking, the Gln84, Lys195, Val182 and Pro184 sites of Crocetin glucosyltransferase 2 were found to be the targets of crocetin binding. By multivariate statistical analysis (PCA and PLS-DA), different saffron samples were clearly distinguished. The results provided the basis for the selection and identification of high quality saffron from different producing areas.

Chemical, Textural and Antioxidant Properties of Oat-Fermented Beverages with Different Starter Lactic Acid Bacteria and Pectin

Currently, starter cultures for fermenting plant-based beverages are not widely available commercially, but producers can use starter cultures for dairy products. Therefore, the aim of this study was to determine the physicochemical, rheological, antioxidant and sensory properties of oat beverages with/without pectin fermented by four different dairy starter cultures. The use of a mono-starter with Lactobacillus bulgaricus or Sreptococcus thermophilus allows for the efficient use of glucose, and more lactic acid is accumulated. The beverage with L. bulgaricus is characterised by high adhesion, syneresis and low cohesiveness, and it has high antioxidant activity and a low sensory profile. Using starter with L. bulgaricus, S. thermophilus and some Lactococcus for fermentation yields a product with high sensory capacity, forming a high-viscosity beverage matrix with low syneresis, high water retention, chewy texture and stickiness. It has been observed that the absence of lactococci and the presence of Lactobacillus casei, L. Rhamnosus and L. paracasei in the starter yields a product with high antioxidant activity, especially in the presence of pectin. The use of pectin significantly improves the viscosity and textural properties of oat yoghurt, enhancing the drink's flavour and giving it body. For many reasons, the use of different commercial starters in the dairy industry results in different viscosities of oat fermented beverages, forming a matrix with different textural, sensory and antioxidant properties.

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.

High-confidence structural annotation of substances via multi-layer molecular network reveals the system-wide constituent alternations in milk interfered with diphenylolpropane

The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, p < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 μg kg-1 1.7 times) and maltodextrin (original: 6.9 μg kg-1 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics.

Se-Pair Search for Deciphering Selenium-Encoded Peptide and a Pyrolysis-Thermolysis Dietary Model for Minimizing Loss of "KKSe(M)R" during Processing

Dietary deficiency of selenium is a global health hazard. Supplementation of organic selenopeptides via food crops is a relatively safe approach. Selenopeptides with heterogeneous selenium-encoded isotopes or a poorly fragmented peptide backbone remain unidentified in site-specific selenoproteomic analysis. Herein, we developed the Se-Pair Search, a UniProtKB-FASTA-independent peptide-matching strategy, exploiting the fragmentation patterns of shared peptide backbones in selenopeptides to optimize spectral interpretation, along with developing new selenosite assignment schemes (steps 1-3) to standardize selenium-localization data reporting for the selenoproteome community and thereby facilitating the discovery of unexpected selenopeptides. Using selenium-biofortified rice under cooking, fermentation, and high-temperature and high-pressure processing conditions as a pyrolysis-thermolysis dietary model, we probed the single-molecule-level kinetic evolution of the novel selenopeptide "KKSe(M)R" with qual-quantitative information on graph-theory-oriented localization calculations, abundance patterns, activation energy, and rate constants at a selenoproteome-wide scale. We ground-truth-annotated thirteen pyrolysis-thermolysis products and inferred four pyrolysis-thermolysis pathways to characterize the formation reactivity of the main intermediate variables of KKSe(M)R and constructed an advanced probe-type ultrasound technique prior to pyrolysis-thermolysis conditions for minimizing loss of KKSe(M)R during processing. Importantly, we reveal the unappreciated pyro-excitation diversion of KKSe(M)R at pyrolysis-thermolysis time and temperature matrices. These findings provide pioneering theoretical guidance for controlling dietary selenium supplementation within the safety thresholds.

High-throughput analysis of hazards in novel food based on the density functional theory and multimodal deep learning

The emergence of cultured meat presents the potential for personalized food additive manufacturing, offering a solution to address future food resource scarcity. Processing raw materials and products in synthetic food products poses challenges in identifying hazards, impacting the entire industrial chain during the industry's further evolution. It is crucial to examine the correlation of biological information at different levels and to reveal the temporal dynamics jointly. Proposed active prevention method includes four aspects: (i) Investigating the molecular-level mechanism underlying the binding and dissociation of hazards with proteins represents a novel approach to mitigate matrix effect. (ii) Identifying distinct fragments is a pivotal advancement toward developing a novel screening strategy for hazards throughout the food chain. (iii) Designing an artificial intelligence model-based approach to acquire multi-dimensional histology data also holds significant potential for various applications. (iv) Integrating multimodal data is a practical approach to enhance evaluation and feedback control accuracy.

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking

The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5-117.1% accuracy surpassed 1.2-14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

Formation and hazard of ethyl carbamate and construction of genetically engineered Saccharomyces cerevisiae strains in Huangjiu (Chinese grain wine)

Huangjiu, a well-known conventional fermented Chinese grain wine, is widely consumed in Asia for its distinct flavor. Trace amounts of ethyl carbamate (EC) may be generated during the fermentation or storage process. The International Agency for Research on Cancer elevated EC to a Class 2A carcinogen, so it is necessary to regulate EC content in Huangjiu. The risk of intake of dietary EC is mainly assessed through the margin of exposure (MOE) recommended by the European Food Safety Authority, with a smaller MOE indicating a higher risk. Interventions are necessary to reduce EC formation. As urea, one of the main precursors of EC formation in Huangjiu, is primarily produced by Saccharomyces cerevisiae through the catabolism of arginine, the construction of dominant engineered fermentation strains is a favorable trend for the future production and application of Huangjiu. This review summarized the formation and carcinogenic mechanism of EC from the perspectives of precursor substances, metabolic pathways after ingestion, and risk assessment. The methods of constructing dominant S. cerevisiae strains in Huangjiu by genetic engineering technology were reviewed, which provided an important theoretical basis for reducing EC content and strengthening practical control of Huangjiu safety, and the future research direction was prospected.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

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.