Fermentation products, together with food components, determine the sense, nutrition, and safety of fermented foods. Traditional methods of fermentation product identification are time-consuming and cumbersome, which cannot meet the increasing need for the identification of the extensive bioactive metabolites produced during food fermentation. Hence, we propose a data-driven integrated platform (FFExplorer, http://www.rxnfinder.org/ffexplorer/) based on machine learning and data on 2,192,862 microbial sequence-encoded enzymes for computational prediction of fermentation products. Using FFExplorer, we explained the mechanism behind the disappearance of spicy taste during pepper fermentation and evaluated the detoxification effects of microbial fermentation for common food contaminants. FFExplorer will provide a valuable reference for inferring bioactive "dark matter" in fermented foods and exploring the application potential of microorganisms.
Summary
Living cell strains have important applications in synthesising their native compounds and potential for use in studies exploring the universal chemical space. Here, we present a web server named as Cell2Chem which accelerates the search for explored compounds in organisms, facilitating investigations of biosynthesis in unexplored chemical spaces. Cell2Chem employs co-occurrence networks and natural language processing to provide a systematic method for linking living organisms to biosynthesised compounds and the processes that produce these compounds. The Cell2Chem platform comprises 40,370 species and 125,212 compounds. Using reaction pathway and enzyme function in silico prediction methods, Cell2Chem reveals possible biosynthetic pathways of compounds and catalytic functions of proteins to expand unexplored biosynthetic chemical spaces. Cell2Chem can help improve biosynthesis research and enhance the efficiency of synthetic biology.
Availability and Implementation
Cell2Chem is available at: http://www.rxnfinder.org/cell2chem/
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