ArticleBMC bioinformatics2017
Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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76 citing papers in PubMed.
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- Natural product biosynthetic potential reflects macroevolutionary diversification within a widely distributed bacterial taxon.mSystems · 2023Article
- iCAZyGFADB: an insect CAZyme and gene function annotation database.Database : the journal of biological databases and curation · 2023Article
- Evidential deep learning for trustworthy prediction of enzyme commission number.Briefings in bioinformatics · 2023Article
- dbCAN3: automated carbohydrate-active enzyme and substrate annotation.Nucleic acids research · 2023Article
- Whole-genome sequencing and comparative genomic analysis of potential biotechnological strains of Trichoderma harzianum, Trichoderma atroviride, and Trichoderma reesei.Molecular genetics and genomics : MGG · 2023Article
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5 authors.
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Abstract
backgroundCarbohydrate-active enzymes are found in all organisms and participate in key biological processes. These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fast and reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interest as demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach not only assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy.
resultsWe identified conserved peptides for all enzyme families in the CAZy database with Peptide Pattern Recognition. The conserved peptides were matched to protein sequence for de novo annotation and functional prediction of carbohydrate-active enzymes with the Hotpep method. Annotation of protein sequences from 12 bacterial and 16 fungal genomes to families with Hotpep had an accuracy of 0.84 (measured as F1-score) compared to semiautomatic annotation by the CAZy database whereas the dbCAN HMM-based method had an accuracy of 0.77 with optimized parameters. Furthermore, Hotpep provided a functional prediction with 86% accuracy for the annotated genes. Hotpep is available as a stand-alone application for MS Windows.
conclusionsHotpep is a state-of-the-art method for automatic annotation and functional prediction of carbohydrate-active enzymes.
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