Computational approach to the systematic prediction of glycolytic abilities: looking into human microbiota

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Guillermo BlancoAnalia Lourenco

Abstract

Glycoside hydrolases are responsible for the enzymatic deconstruction of complex carbohydrates. This work introduces a new method to predict glycolytic abilities in sequenced genomes and thus, gain a better understanding of how to target specific carbohydrates and identify potentially interesting sources of specialised enzymes. Sequence alignment enables systematic genome screening against organisms whose glycolytic abilities have been manually curated by experts. Clustering of homology scores helps identify organisms that share common abilities as well as the most promising organisms regarding specific glycolytic abilities. The method was applied to members of the bacterial families Ruminococcaceae, Eubacteriaceae, and Lachnospiraceae, which hold major representatives of the human gut microbiota. The method predicted the potential presence of glycoside hydrolases in 1701 species of these genera, i.e. 320 unique glycoside hydrolases in 221 metabolic pathways. Here, the validity and practical usefulness of the method is discussed based on the predictions obtained for members of the genus Ruminococcus. Results were consistent with existing literature and offer useful, complementary insights to comparative genomics and physiologic...Continue Reading

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