The effect of superheated steam (SHS) treatment on the quality characteristics of rape bee pollen were studied, and the efficiency of inactivation and inhibition of lipid oxidation were analyzed to investigate the differences between SHS and cobalt-60 isotope (⁶⁰Co) radiation treatment. The number of total plate count (TPC) and mold colonies (MC) remained within the limits of the standards after SHS treatment at 140 ℃ for 2 min. Neither TPC nor MC were detected after ⁶⁰Co irradiation. Peroxidase (POD) and polyphenol oxidase (PPO) activities significantly decreased with increasing temperature and duration of SHS, while ⁶⁰Co radiation completely inactivated PPO. Compared to ⁶⁰Co radiation, SHS treatment inhibited the deterioration of rape bee pollen by avoiding hydroperoxide production and lipid oxidation due to lack of oxygen. These results suggested SHS under 140 ℃ for 2 min was the most suitable to inactivate the microorganisms and enzymes in rape bee pollen with minimal lipid oxidation.

Propolis is a resinous complex mixture made from plant resins collected by worker bees and mixed with their own secretions. It is rich in polyphenols and flavonoids and thus has a wide range of biological activities and is considered a functional source for promoting human health. However, propolis and its bioactive compounds have poor water solubility, rapid and intense metabolism, and low oral bioavailability, which limits their wide application. In this paper, the main bioactive substances in propolis were summarized, and the biological characteristics and therapeutic potential of propolis and its bioactive substances were discussed. In addition, this paper discussed the factors affecting the bioavailability of propolis and its functional ingredients, focusing on the research progress in improving the bioavailability and bioactivity of propolis and its functional ingredients using nanoencapsulation technology. Finally, the current situation of the global propolis market and the applications of propolis products in the pharmaceutical, food, cosmetic and other industrial fields were discussed, providing useful references for promoting the development of the propolis industry.

To fully utilize Tenebrio molitor (TM) at a high value, Tenebrio molitor protein (TMP) hydrolysates were obtained from the novel insect food resource TM after extracting its proteins for compound protease hydrolysis. In vitro simulated digestion was conducted to increase the angiotensin I-converting enzyme (ACE) inhibitory ability of TMP. Based on the experimental data, peptidomics identification of TMP hydrolysates was performed using UHPLC-ESIHRMS/MS. Four novel ACE-inhibitory peptides (ACEIPs) were obtained using AI tools and in vitro activity validation, namely YPGLQ, PGLPQ, MFGPQ and NLGPR, with IC₅₀ values of 0.8560 ± 0.0090 mmol/L, 1.0300 ± 0.0356 mmol/L, 0.8558 ± 0.0204 mmol/L, and 0.4709 ± 0.0391 mmol/L, respectively. The inhibition patterns of ACEIPs were analyzed by inhibition kinetics as competitive, uncompetitive, mixed, and non-competitive inhibition patterns. The result of CD spectra revealed that the original structure of ACE has been loosened by ACEIPs. The bindings of ACEIPs to ACE were spontaneous, and they had fluorescence quenching effects on ACE. Molecular docking and MD simulation indicated hydrogen bonds, hydrophobic forces, and electrostatic forces are the main forces between them and ACE. In this study, we tried to extract ACEIPs from TMP using a three-stage enzymatic digestion process, elucidated their inhibition mechanism, and demonstrated their biosafety using AI tools, which provided a new strategy for the preparation of ACEIPs and guided the expansion of TMP application.

To shorten the complex and time-consuming process of the identification method of the traditional food angiotensin-I-converting enzyme (ACE-I) inhibitory peptides, we propose AHTPeptideFusion based on a segmented fusion with the protein language model and deep learning. The statistical analysis found that hydrophobic amino acids, N-terminal valine is a dominant amino acid in the activity of ACE-I inhibitory peptides. In 12 machine learning (ML) algorithms, the transformer outperformed the other 11 models, with the best performance in predicting short and medium peptides. In the external dataset, AHTPeptideFusion fused by transformer and random forest (RF) showed excellent performance (accuracy > 0.9) in predicting ACE-I inhibitory peptides with lengths ranging from 2 to 15 amino acid residues and different activity distributions, and the reliability and accuracy of AHTPeptideFusion was demonstrated by synthetic peptide and ACE-I inhibition experiments. In addition, hydrogen bonding and electrostatic interaction between 4 synthetic peptides and active residues of ACE-I were found by molecular docking. To further explore the ACE-I inhibitory peptides from animal-derived foods, we established an automated pipeline consisting of the trinity of proteomics, virtual enzymatic digestion and AHTPeptideFusion, and tapped the ACE-I inhibitory peptide released from royal jelly after digestion in the gastrointestinal tract. In conclusion, this computational pipeline will become a powerful screening tool for active peptides from animal-derived foods, which can help food scientists accelerate the mining and design of active peptides from animal-derived foods. Overall, AHTPeptideFusion will be a powerful ACE-I inhibitor peptide prediction tool, it can help food scientists accelerate the mining and design of ACE-I inhibitory peptides.