In this study, effects of different pretreatment methods on the enzymatic digestibility of Pennisetum alopecuroides, a ubiquitous wild grass in China, were investigated to evaluate its potential as a feedstock for biofuel production. The stalk samples were separately pretreated with H₂SO₄, NaOH and FeCl₃ solutions of different concentrations at 120 °C for 30 min, after which enzymatic hydrolysis was conducted to measure the digestibility of pretreated samples. Results demonstrated that different pretreatments were effective at removing hemicellulose, among which ferric chloride pretreatment (FCP) gave the highest soluble sugar recovery (200.2 mg/g raw stalk) from the pretreatment stage. In comparison with FCP and dilute acid pretreatment (DAP), dilute alkaline pretreatment (DALP) induced much higher delignification and stronger morphological changes of the biomass, making it more accessible to hydrolysis enzymes. As a result, DALP using 1.2% NaOH showed the highest total soluble sugar yield through the whole process from pretreatment to enzymatic hydrolysis (508.5 mg/g raw stalk). The present work indicates that DALP and FCP have the potential to enhance the effective bioconversion of lignocellulosic biomass like P. alopecuroides, hence making this material a valuable and promising energy plant.

Deep learning has great potential in the field of functional peptide prediction. This study combines metagenomics and deep learning to efficiently discover potential umami peptides in fermented sausages. A candidate peptide library was generated using metagenomic data from fermented sausages, an integrated deep learning model was constructed for prediction, and SHAP (SHapley Additive exPlanations) interpretability analysis was performed to elucidate the key amino acid features and contributions of the model in predicting umami peptides, screening the top ten peptides with the highest predicted probability. Subsequently, molecular docking was performed to assess the binding stability of these peptides with the umami receptor T1R1/T1R3, selecting the three peptides DDSMAATGL, DGEEDASM, and DEEEVDI with the most stable binding for further study. Docking analysis revealed the important roles of the key receptor residues Glu301, Arg277, Lys328, and His71 in hydrogen bond formation. Molecular dynamics simulations validated the robust integrity of the peptide–receptor associations. Finally, sensory evaluation demonstrated that these three peptides possessed significant umami characteristics, with low umami thresholds (0.11, 0.37, and 0.44 mg/mL, respectively). This study, based on metagenomics and deep learning, provides a high-throughput strategy for the discovery and validation of functional peptides.

Red wine is a well-known alcoholic beverage, and is known to have phenolic compounds (PCs), which contribute to its antioxidant activity and have other beneficial advantages for human health. The aim of this study was to evaluate the effect of the simulated gastro-intestinal digestion and the Caco-2 transepithelial transport assay on the PCs, bioavailability, and the antioxidant capacity of red wines. The contents of PCs in red wine were significantly reduced during most of the digestion phases. Phenolic acid had the greatest permeability, while the flavonols had the weakest. The bioavailability of PCs ranged from 2.08 to 24.01%. The result of the partial least squares structural equation model showed that the three phenols were positively correlated with the antioxidant activity of red wine. The contribution of anthocyanins was the largest (0.8667).

As the issue of traditional antibiotic resistance continues to worsen, exploring new antimicrobial substances has become crucial to addressing this challenge. Antimicrobial peptides (AMPs), recognized for their low resistance levels and minimal bacterial mutation frequencies, have garnered significant attention from researchers. However, traditional screening methods for AMPs are inefficient and costly. This study proposes a combined AMP screening model based on long short-term memory (LSTM) neural networks with an attention mechanism. By analyzing the characteristics of peptide segments, which are simulated enzymatic hydrolysis products of proteins expressed in sludge microbial genomes, the model accurately identifies peptide segments with potential antimicrobial activity. Molecular docking and dynamic simulation results validate three potential antimicrobial peptide candidates: LLPRLLARRY, GVREIHGLNPGGCLHTVRLVCR, and FRTTLAPHVLTRLLAPCW. These candidates exhibit high binding stability and affinity with target proteins, confirming the efficiency of the proposed AMP screening model.