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

Abstract

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.

Keywords: Antibacterial activity; Bacteriocin; Machine learning; Microbiome; Molecular dynamics simulation; Structure prediction.