Carbohydrate-active enzymes (CAZymes) are central to carbohydrate metabolism, yet their functional annotation is typically restricted to catalytic CAZy domains, overlooking the broader multidomain architectures in which these domains operate. Here, I present GeneHunt2, a scalable framework for multidomain annotation of CAZymes that integrates curated HMM profiles from dbCAN and Pfam into a unified, deduplicated database, enabling systematic identification of both CAZy and non-CAZy domains. After confirming the robust recovery of CAZy domain assignments using GeneHunt2, I investigated the detailed multidomain architecture of over 3.75 million CAZyme sequences: more than 40% were multidomain, and non-CAZy partner domains constituted a substantial fraction of detected partner domains. Using a quantitative framework that combines co-occurrence enrichment, domain adjacency, positional bias, and partner-specificity scoring, I next distinguished family-specific modules, auxiliary domains, and promiscuous partners. This approach recapitulates known CAZyme-CBM relationships and extends beyond CAZy definitions by identifying numerous Pfam domains including many domains of unknown function (DUFs) that are specifically and non-randomly associated with particular CAZy families. By enabling reproducible, multidomain-aware annotation, GeneHunt2 facilitates data-driven hypotheses about poorly characterized domains and widens the functional interpretation of carbohydrate-active proteins beyond their catalytic cores.
Abstract Motivation Functional characterization of microbiomes often relies on the sequencing of metagenomic DNA extracted from environmental samples, with current approaches using metagenome-assembled genomes (MAGs). Although glycoside hydrolases (GHs) are central to carbon cycling, accurate annotation of GHs in metagenomic datasets remains challenging due to the multidomain architecture of carbohydrate-active enzymes and the prevalence of unassembled short reads due to limitations in the MAG-generation process. Results Here, we present CAZyOGH (CAZymes Open-source GH annotation), a curated reference database for the domain-specific identification of 135 protein domains spanning 99 GH families with well-defined catalytic domain signatures. CAZyOGH focuses on individual GH domains, enabling robust annotation of both assembled and unassembled metagenomic data. We validated CAZyOGH by reanalyzing genomes listed in CAZy db, where predicted GH profiles closely matched reported values. Next, we used CAZyOGH to analyze 12 human gut metagenomes and 12 newly sequenced soil microbiomes to reveal environment-specific GH repertoires. By accurately detecting catalytic domains independent of the genomic context, CAZyOGH improves sensitivity and specificity in short-read metagenomic annotation. This framework provides a scalable and reproducible approach to investigate carbohydrate-active enzymes across ecosystems, advancing our capacity to characterize microbial functional potential in global carbon cycling. Availability and implementation CAZyOGH data is available on figshare (https://figshare.com/projects/CAZyO_GH/267770).
N. Griffin, Alison E Hughes, D. S. Erdody et al.· Bioinformatics Advances· 0 citations