A Bioinformatics Approach to Identifying Radical SAM (S-Adenosyl-L-Methionine) Enzymes

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Date
2020-06-03
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Virginia Tech
Abstract

Radical SAM enzymes are ancient, essential enzymes. They perform radical chemical reactions in virtually all living organisms and are involved in producing antibiotics, generating greenhouse gases, human health, and likely many other essential roles that have yet to be established. A wide variety of reactions have been characterized from this group of enzymes, including hydrogen abstractions, the transferring of methylthio groups, complex cyclization and rearrangement reactions, and others. However, many radical SAM enzymes have yet to be identified or characterized. There have been great leaps forward in the amount of enzyme sequences that are available in public databases, but experiments to investigate what chemical reactions the enzymes perform take a great deal of time. In our work, we utilize Hidden Markov Models to identify possible radical SAM enzymes and predict their possible functions through BLAST alignments and homology modelling. We also explore their distribution across the tree of life and determine how it is correlated with organism oxygen tolerances, because the core iron-sulfur cluster is oxygen sensitive. Trends in the abundances of radical SAM enzymes depending on oxygen tolerances were more apparent in prokaryotes than in eukaryotes. Although eukaryotes tend to have fewer radical SAM enzymes than prokaryotes, we were able to analyze uncharacterized radical SAM enzymes from both an aerobic eukaryote (Entamoeba histolytica) and a eukaryote capable of oxygenic photosynthesis (Gossypium barbadense), and predict the reactions they catalyze. This work sets the stage for the functional characterization of these essential yet elusive enzymes in future laboratory experiments.

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Bioinformatics, Radical Biochemistry, Enzymology
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