ArticleBioinformatics (Oxford, England)2015
Functional classification of CATH superfamilies: a domain-based approach for protein function annotation.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 69 papers.
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69 citing papers in PubMed.
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- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
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- Sequence-based comparative secretome analysis reveals conserved core effectors and host lineage-specific divergence between monocot- and dicot-associated powdery mildew lineages.Frontiers in plant science · 2026Article
- A scalable equivariant graph network framework for precise protein function prediction.Genome biology · 2025Article
- Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins.G3 (Bethesda, Md.) · 2025Article
- Evidence for vitellogenin DNA-binding in honey bees.Protein science : a publication of the Protein Society · 2025Article
- Structure-guided sequence representation learning for generalizable protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- GOBoost: leveraging long-tail gene ontology terms for accurate protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- Computational studies reveal structural characterization and novel families of Puccinia striiformis f. sp. tritici effectors.PLoS computational biology · 2025Article
- MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.Bioinformatics (Oxford, England) · 2025Article
- InterPro: the protein sequence classification resource in 2025.Nucleic acids research · 2025Article
- CATHe2: Enhanced CATH superfamily detection using ProstT5 and structural alphabets.Biology methods & protocols · 2025Article
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
motivationComputational approaches that can predict protein functions are essential to bridge the widening function annotation gap especially since <1.0% of all proteins in UniProtKB have been experimentally characterized. We present a domain-based method for protein function classification and prediction of functional sites that exploits functional sub-classification of CATH superfamilies. The superfamilies are sub-classified into functional families (FunFams) using a hierarchical clustering algorithm supervised by a new classification method, FunFHMMer.
resultsFunFHMMer generates more functionally coherent groupings of protein sequences than other domain-based protein classifications. This has been validated using known functional information. The conserved positions predicted by the FunFams are also found to be enriched in known functional residues. Moreover, the functional annotations provided by the FunFams are found to be more precise than other domain-based resources. FunFHMMer currently identifies 110,439 FunFams in 2735 superfamilies which can be used to functionally annotate>16 million domain sequences. AVAILABILITY AND IMPLEMENTATION: All FunFam annotation data are made available through the CATH webpages (http://www.cathdb.info). The FunFHMMer webserver (http://www.cathdb.info/search/by_funfhmmer) allows users to submit query sequences for assignment to a CATH FunFam. CONTACT: sayoni.das.12@ucl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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