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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Associating Protein Domains with Biological Functions: A Tripartite Network Approach
verfasst von : Elena Rojano, James Richard Perkins, Ian Sillitoe, Christine Orengo, Juan Antonio García Ranea, Pedro Seoane
Erschienen in: Bioinformatics and Biomedical Engineering
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Abstract
Protein domains are key determinants of protein function. However, a large number of domains have no recorded functional annotation. These domains of unknown function (DUFs) are a recognised problem and efforts have been made to remedy this situation, including the use of data such as structural and sequence similarity and annotation data such as that of Gene Ontology (GO) and The Enzyme Commission.
Here, we present a new approach based on tripartite network analysis to assign functional terms to DUFs. We combine functional annotation at the protein level, taken from GO, KEGG, Reactome and UniPathway, with structural domain annotation, taken from the CATH-Gene3D resource. We validate our method using 10-fold cross-validation and find it performs well when assigning annotation from the UniPathway, Reactome and GO resources, but less well for KEGG. We also explored using a finer functional subclassification of CATH superfamilies (FunFams) but these families were found to be too specific in this context.