Abstract
BackgroundManual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function.ResultsWe present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters.ConclusionsInterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN).
Highlights
Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing
Despite some known limitations, such as some inconsistencies between the rules set by the nomenclature committee and the actual class definitions [7], we use the NC-IUBMB Enzyme Commission (EC) nomenclature to define enzymatic reactions, as it is the current standard for enzyme function classification
For each taxonomic domain we have investigated the individual proteome having most proteins in the SwissProt KEGG set: Methanocaldococcus jannaschii for archaea, Escherichia coli for bacteria, Schizosaccharomyces pombe for fungi, Drosophila melanogaster for invertebrates, Arabidopsys thaliana for plants, Homo sapiens for vertebrates
Summary
Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Assigning enzymatic function to the proteins in a genome is one of the first essential steps of metabolic reconstruction, important for biology, medicine, industrial production and environmental studies. At the current rate of genome sequencing and manual annotation, manual curation will never complete the functional annotation of all available proteomes [2]. In this work we propose and evaluate a method to automatically predict the enzymatic functions. Despite some known limitations, such as some inconsistencies between the rules set by the nomenclature committee and the actual class definitions [7], we use the NC-IUBMB Enzyme Commission (EC) nomenclature to define enzymatic reactions, as it is the current standard for enzyme function classification. The first three digits represent an increasingly detailed definition of reaction class, while the last digit represents the accepted substrates
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