GeneHunt for rapid domain-specific annotation of glycoside hydrolases

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.

MetaGeneHunt for protein domain annotation in short-read metagenomes

“…Next, samples from the intestine (I) formed a more homogeneous and deeply branched cluster enriched in sequences from the abundant GH2 family. Most of the biochemically-characterized members of this GH family encode putative β-galactosidases 15,29 . Similarly, sequences encoding GH5 (cellulases), GH28 (polygalacturonases), GH29 (α-L-fucosidases), and GH97 (α-galactosidases) were more characteristic of the intestine.…”

“…To address these problems, we designed MetaGeneHunt to perform precise annotation of protein domains in short-reads metagenomes retrieved from MG-RAST. MetaGeneHunt combines short-read local alignment, provided by MG-RAST, with precise PFam-based 14 protein domain identification in the M5nr database 15 to identify protein domains in publicly accessible datasets. Here, we focused on microbial glycoside hydrolases (GHs) as these enzymes are essential 16 for the processing of carbohydrates in mammals' gut 1,2 and across environments 3 where they support the carbon cycling.…”

“…Here, we focused on microbial glycoside hydrolases (GHs) as these enzymes are essential 16 for the processing of carbohydrates in mammals' gut 1,2 and across environments 3 where they support the carbon cycling. Like other carbohydrate active enzymes (CAZymes) and many other protein families, GHs are known for their complex multidomain architecture 15 . Although most biochemically characterized GH-enzymes consist of single-domain GHs (SDGHs), the bioinformatic analysis of sequenced genomes and assembled metagenomes has highlighted the complexity and abundance of multi-domain GHs (MDGHs) 12,15,17 .…”

“…Like other carbohydrate active enzymes (CAZymes) and many other protein families, GHs are known for their complex multidomain architecture 15 . Although most biochemically characterized GH-enzymes consist of single-domain GHs (SDGHs), the bioinformatic analysis of sequenced genomes and assembled metagenomes has highlighted the complexity and abundance of multi-domain GHs (MDGHs) 12,15,17 . For example, ~60% of identified α-amylases from GH13 contain multiple domains.…”

“…For example, ~60% of identified α-amylases from GH13 contain multiple domains. Similarly up to ~25% of known enzymes active on cellulose, xylan, and chitin are MDGHs 15 . In addition, many "non-CAZy" domains, including some α/β-hydrolases and many domains of unknown function (DUFs), are linked to CAZy domains.…”