Predicting protein function from domain content
2008 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 24, no 15, 1681-1687 p.Article in journal (Refereed) Published
MOTIVATION: Computational assignment of protein function may be the single most vital application of bioinformatics in the post-genome era. These assignments are made based on various protein features, where one is the presence of identifiable domains. The relationship between protein domain content and function is important to investigate, to understand how domain combinations encode complex functions.
RESULTS: Two different models are presented on how protein domain combinations yield specific functions: one rule-based and one probabilistic. We demonstrate how these are useful for Gene Ontology annotation transfer. The first is an intuitive generalization of the Pfam2GO mapping, and detects cases of strict functional implications of sets of domains. The second uses a probabilistic model to represent the relationship between domain content and annotation terms, and was found to be better suited for incomplete training sets. We implemented these models as predictors of Gene Ontology functional annotation terms. Both predictors were more accurate than conventional best BLAST-hit annotation transfer and more sensitive than a single-domain model on a large-scale dataset. We present a number of cases where combinations of Pfam-A protein domains predict functional terms that do not follow from the individual domains.
AVAILABILITY: Scripts and documentation are available for download at http://sonnhammer.sbc.su.se/multipfam2go_source_docs.tar
Place, publisher, year, edition, pages
2008. Vol. 24, no 15, 1681-1687 p.
Amino Acid Sequence, Computer Simulation, Models; Biological, Models; Chemical, Molecular Sequence Data, Protein Structure; Tertiary, Proteins/*chemistry/classification/*metabolism, Sequence Analysis; Protein/*methods, Structure-Activity Relationship
Bioinformatics and Systems Biology
Research subject Biochemistry with Emphasis on Theoretical Chemistry
IdentifiersURN: urn:nbn:se:su:diva-14973DOI: 10.1093/bioinformatics/btn312ISI: 000257956600005PubMedID: 18591194OAI: oai:DiVA.org:su-14973DiVA: diva2:181493