Study And Platform Construction Based On The Screening Of Antimicrobial Peptide-producing Lactic Acid Bacteria And Prediction Of Lactic Acid Bacteria Interactions

Lactic acid bacteria are microorganisms recognized as safe for consumption.The antimicrobial peptide produced by these bacteria during their metabolism can inhibit pathogenic bacteria,making them highly suitable for applications in food production,livestock breeding,and biomedicine.Lactobacillus bulgaricus and Streptococcus thermophilus are two major strains of lactic acid bacteria that exhibit symbiotic characteristics and are widely used as starter cultures in the production of fermented dairy products.However,the traditional biological experiments used for screening antimicrobial peptide-producing bacteria or for screening starter culture interaction combinations are time-consuming and laborious,negatively affecting downstream industrial production.The use of computer artificial intelligence technology can overcome these shortcomings by enabling rapid preliminary screening of large quantities of strains,thereby improving screening efficiency.This study was conducted on two fronts:(1)The screening of strains of antimicrobial peptide-producing lactic acid bacteria using deep learning and bioinformatics tools.A combination of the bioinformatics tool Prodigal,multiple sequence alignment,a graph neural network model,and Hmmer-based domain search was used to screen for antimicrobial peptide-producing lactic acid bacteria at the strain level and genome level.The whole genome sequence of lactic acid bacteria was sequentially predicted by Prodigal and the three pathways were combined to determine whether the lactic acid bacteria to be tested produced antimicrobial peptides and their specific antimicrobial peptide domains.(2)The prediction of whether lactic acid bacteria interact with each other using machine learning methods.For the initial screening of interaction combinations,a Lactobacillus Interaction Symbiosis Prediction Study was carried out using the clustering algorithm based on KEGG metabolic pathway data and gene presence and deletion matrix.The Laplace normalized least squares operation was then used to optimize the interaction prediction results.Based on the experimental results,a machine learning model was constructed to verify the optimization effect,10%-20% improvement in clustering accuracy compared to optimized methods and a complete screening process for the combination of interactive strains was realized.The study successfully screened 156,9,and 66 strains of antimicrobial peptideproducing Lactobacillus bulgaricus,Streptococcus thermophilus,and Lactobacillus plantarum,respectively,from a total of 188,182,and 81 strains.The study also predicted29 candidate starter culture strain combinations from 38,728 combinations of Lactobacillus bulgaricus and Streptococcus thermophilus.The preliminary screening of lactic acid-producing bacteria antimicrobial peptide strains and mining domain protein fragments that inhibit the activity of gram-positive and gram-negative bacteria showed promising results with certain accuracy,as compared with biological experimental results published in the literature.To facilitate researchers in browsing data and using related functions,this study built a user-friendly open lactic acid bacteria function platform based on the Spring Boot framework and deployed it to the cloud server.