Evidence map›Paper›PMID 35033002›Full record

ArticleBMC bioinformatics2022

Assigning protein function from domain-function associations using DomFun.

Elena Rojano, Fernando M Jabato, James R Perkins, José Córdoba-Caballero, Federico García-Criado, Ian Sillitoe, Christine Orengo, Juan A G Ranea, Pedro Seoane-Zonjic

Abstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024
    Review
  3. Article
  4. Mutual annotation-based prediction of protein domain functions with Domain2GO.Protein science : a publication of the Protein Society · 2024
    Article
  5. Article
  6. Review
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Elena RojanoDepartment of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.
Fernando M JabatoDepartment of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.
James R PerkinsDepartment of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain. jimrperkins@gmail.com.ORCID http://orcid.org/0000-0003-4108-096X
José Córdoba-CaballeroDepartment of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.
Federico García-CriadoDepartment of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.
Ian SillitoeDepartment of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.
Christine OrengoDepartment of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.
Juan A G Ranea *Department of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.
Pedro Seoane-Zonjic *Department of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.

Funding

Junta de Andalucia PAIDI 2020:PY20-00372Junta de Andalucía PI-0075-2017Junta de Andalucía UMA18-FEDERJA-102Ministerio de Ciencia, Innovaciín y Universidades PID2019-105010RB-I00Ministerio de Ciencia, Innovaciín y Universidades PID2019-108096RB-C21Ministerio de Ciencia, Innovaciín y Universidades (ES) SAF2016-78041-C2-1-RUniversidad de Mílaga I Ayudas del Plan Propio, D.2
6 · The paper itself

Abstract

backgroundProtein 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.

resultsWe 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 [Formula: see text] and [Formula: see text] 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.

conclusionsDomFun 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 .

Indexed as

Computational BiologyProteinsDatabases, ProteinGene OntologyMolecular Sequence AnnotationProteinsCAFACATHDomFunFunction predictionProtein domains

Identifiers

PMID35033002
PMCPMC8761305

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.