Assigning protein function from domain‑function associations using DomFun.

dc.centroFacultad de Ciencias
dc.contributor.authorRojano Rivera, María Elena
dc.contributor.authorMoreno-Jabato, Fernando
dc.contributor.authorPerkins, James R.
dc.contributor.authorCórdoba-Caballero, José
dc.contributor.authorGarcía-Criado, Federico
dc.contributor.authorSillitoe, Ian
dc.contributor.authorOrengo, Christine
dc.contributor.authorGarcía-Ranea, Juan Antonio
dc.contributor.authorSeoane-Zonjic, Pedro
dc.date.accessioned2026-05-04T09:08:46Z
dc.date.issued2022-01-15
dc.departamentoBiología Molecular y Bioquímica
dc.description.abstractProtein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of and We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer’s method led to the top performance in almost all scenarios. DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun. Code maintained at https://github.com/ElenaRojano/DomFun. Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project.
dc.identifier.citationRojano, E., Jabato, F.M., Perkins, J.R. et al. Assigning protein function from domain-function associations using DomFun. BMC Bioinformatics 23, 43 (2022). https://doi.org/10.1186/s12859-022-04565-6
dc.identifier.doi10.1186/s12859-022-04565-6
dc.identifier.issn1471-2105
dc.identifier.urihttps://hdl.handle.net/10630/46540
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBiología computacional
dc.subject.otherFunction prediction
dc.subject.otherCATH
dc.subject.otherDomFun
dc.subject.otherProtein domains
dc.subject.otherCAFA
dc.titleAssigning protein function from domain‑function associations using DomFun.
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
relation.isAuthorOfPublication8c8b05f2-a296-4ec5-aa57-f77f60a303a8
relation.isAuthorOfPublication.latestForDiscovery8c8b05f2-a296-4ec5-aa57-f77f60a303a8

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