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Abstract
The identification of glycoside hydrolases (GHs) for efficient polysaccharide deconstruction is essential for the development of biofuels. Here, we investigate the potential of sequential HMM-profile identification for the rapid and precise identification of the multi-domain architecture of GHs from various datasets. First, as a validation, we successfully reannotated >98% of the biochemically characterized enzymes listed on the CAZy database. Next, we analyzed the 43 million non-redundant sequences from the M5nr data and identified 322,068 unique GHs. Finally, we searched 129 assembled metagenomes retrieved from MG-RAST for environmental GHs and identified 160,790 additional enzymes. Although most identified sequences corresponded to single domain enzymes, many contained several domains, including known accessory domains and some domains never identified in association with GH. Several sequences displayed multiple catalytic domains and few of these potential multi-activity proteins combined potentially synergistic domains. Finally, we produced and confirmed the biochemical activities of a GH5-GH10 cellulase-xylanase and a GH11-CE4 xylanase-esterase. Globally, this “gene to enzyme pipeline” provides a rationale for mining large datasets in order to identify new catalysts combining unique properties for the efficient deconstruction of polysaccharides.
Highlights
The identification of glycoside hydrolases (GHs) for efficient polysaccharide deconstruction is essential for the development of biofuels
In order to identify new catalysts for biomass degradation, we examined the performance of sequential Hidden Markov Model (HMM) identifications[28] combined with publicly accessible HMM-profiles from the PFam database[29], here referred to as the GeneHunt approach[2,30], to detect GH-sequences and investigate their detailed architecture[2]
In order to evaluate the GeneHunt approach, weannotated the sequences of biochemically characterized GHs listed on the Carbohydrate-Active Enzymes (CAZy) database
Summary
The identification of glycoside hydrolases (GHs) for efficient polysaccharide deconstruction is essential for the development of biofuels. We produced and confirmed the biochemical activities of a GH5-GH10 cellulase-xylanase and a GH11-CE4 xylanase-esterase This “gene to enzyme pipeline” provides a rationale for mining large datasets in order to identify new catalysts combining unique properties for the efficient deconstruction of polysaccharides. In order to identify new catalysts for biomass degradation, we examined the performance of sequential Hidden Markov Model (HMM) identifications[28] combined with publicly accessible HMM-profiles from the PFam database[29], here referred to as the GeneHunt approach[2,30], to detect GH-sequences and investigate their detailed architecture (i.e., the precise domain organization of MDGHs)[2]. We identified the detailed multi-domain architecture of GH proteins in assembled, publicly accessible, metagenomes from www.nature.com/scientificreports/
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