ArticleNucleic acids research2004
EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference.
Article in Nucleic acids research, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer.Nature communications · 2026Article
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Review
- Evidential deep learning for trustworthy prediction of enzyme commission number.Briefings in bioinformatics · 2023Article
- Improving automatic GO annotation with semantic similarity.BMC bioinformatics · 2022Article
- Structure and dynamics of membrane protein in SARS-CoV-2.Journal of biomolecular structure & dynamics · 2022Article
- Transcriptomic analysis of Rhodococcus opacus R7 grown on polyethylene by RNA-seq.Scientific reports · 2021Article
- Machine learning differentiates enzymatic and non-enzymatic metals in proteins.Nature communications · 2021Article
- Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study.BMC bioinformatics · 2020Article
- Article
- GrAPFI: predicting enzymatic function of proteins from domain similarity graphs.BMC bioinformatics · 2020Article
- Chicken Meat-Associated Enterococci: Influence of Agricultural Antibiotic Use and Connection to the Clinic.Applied and environmental microbiology · 2019Article
- Predicting Sequence Features, Function, and Structure of Proteins Using MESSA.Current protocols in bioinformatics · 2019Article
- Impact of antibiotic treatment and host innate immune pressure on enterococcal adaptation in the human bloodstream.Science translational medicine · 2019Article
- Evidence for Highly Variable, Region-Specific Patterns of T-Cell Epitope Mutations Accumulating inFrontiers in immunology · 2019Article
- DEEPre: sequence-based enzyme EC number prediction by deep learning.Bioinformatics (Oxford, England) · 2018Article
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.Frontiers in genetics · 2018Article
- Confronting the catalytic dark matter encoded by sequenced genomes.Nucleic acids research · 2017Article
- Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.Scientific reports · 2017Article
- The genome of Onchocerca volvulus, agent of river blindness.Nature microbiology · 2016Article
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3 authors.
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Abstract
EFICAz (Enzyme Function Inference by Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from four different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation-controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, and (iv) recognition of multiple Prosite patterns of high specificity. For FDR (i.e. conserved positions in an enzyme family that discriminate between true and false members of the family) identification, we have developed an Evolutionary Footprinting method that uses evolutionary information from homofunctional and heterofunctional multiple sequence alignments associated with an enzyme family. The FDRs show a significant correlation with annotated active site residues. In a jackknife test, EFICAz shows high accuracy (92%) and sensitivity (82%) for predicting four EC digits in testing sequences that are <40% identical to any member of the corresponding training set. Applied to Escherichia coli genome, EFICAz assigns more detailed enzymatic function than KEGG, and generates numerous novel predictions.
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