Identifying and characterization of novel broad-spectrum bacteriocins from the Shanxi aged vinegar microbiome: Machine learning, molecular simulation, and activity validation.

Shanxi aged vinegar microbiome encodes a wide variety of bacteriocins. The aim of this study was to mine, screen and characterize novel broad-spectrum bacteriocins from the large-scale microbiome data of Shanxi aged vinegar through machine learning, molecular simulation and activity validation. A total of 158 potential bacteriocins were innovatively mined from 117,552 representative genes based on metatranscriptomic information from the Shanxi aged vinegar microbiome using machine learning techniques and 12 microorganisms were identified to secrete bacteriocins at the genus level. Subsequently, employing AlphaFold2 structure prediction and molecular dynamics simulations, eight bacteriocins with high stability were further screened, and all of them were confirmed to have bacteriostatic activity by the Escherichia coli BL21 expression system. Then, gene_386319 (named LAB-3) and gene_403047 (named LAB-4) with the strongest antibacterial activities were purified by two-step methods and analyzed by mass spectrometry. The two bacteriocins have broad-spectrum antimicrobial activity with minimum inhibitory concentration values of 6.79 µg/mL-15.31 µg/mL against Staphylococcus aureus and Escherichia coli. Furthermore, molecular docking analysis indicated that LAB-3 and LAB-4 could interact with dihydrofolate reductase through hydrogen bonds, salt-bridge forces and hydrophobic forces. These findings suggested that the two bacteriocins could be considered as promising broad-spectrum antimicrobial agents.

Screening and characterization of a novel antifreeze peptide from silver carp muscle hydrolysate.

This study aimed to screen and characterize antifreeze peptides from silver carp muscle hydrolysate (SCMH). The SCMH was initially fractionated by ultrafiltration, and the resultant SCMH-IV (<10 kDa) showing 90 % of yeast survival rate was subsequently separated into four fractions using ion-exchange chromatography. The fraction with the best antifreeze activity was further analyzed by liquid chromatography-tandem mass spectrometry. A total of 514 peptides were identified, of which a novel antifreeze peptide (Sc-AFP, KAADSFNHKAFFAKVG) with a thermal hysteresis activity of 0.87 ℃ was selected. The parvalbumin-derived Sc-AFP showed an alanine-rich, α-helical and amphipathic character. Based on molecular dynamics simulations, the Sc-AFP could interact with 48 water molecules via hydrogen bonds, and could be adsorbed onto the ice surface through a total of 21 hydrogen bonds mainly linked to the lysine residues. This could account for its antifreeze properties via preventing the formation and growth of ice crystals.