Developing agrochemical products with favorable environmental profiles includes studies conducted to understand the biotransformation pathways and rate of metabolism in human, animal, plant, and soil systems. Often these studies generate metabolites in trace amounts that are difficult to identify and synthesize. Microbial biotransformation is a powerful approach that can provide access to such molecules. Microbial biocatalysts are excellent mimics for other metabolic systems and can be rapidly screened to generate a rich diversity of structural analogs. While very useful in accessing specific metabolites, this approach also generates a plethora of novel compounds that can help better understand the biotransformation pathways of the products being studied. Recent developments in untargeted metabolomics and computational mass spectrometry have paved the path to predict and identify these metabolites. Molecular networking is a cutting-edge strategy that exploits the structural relatedness of metabolites to predict different biotransformations. This presentation will include examples where a molecule was screened through the biotransformation panel in a high throughput fashion and analyzed using high resolution LC-MS/MS systems, to provide information on the metabolites formed from the parent molecule in the microbial cultures. The generation of environmentally relevant metabolites could thus be simulated by utilizing molecular networking and other advanced in silico tools. This approach was used to aid in structure elucidation of many novel metabolites.

Mass spectrometry based molecular networking to predict microbial metabolism for identification of challenging environmental metabolites

ACS Fall 2021

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