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ENZYMAP: Exploiting Protein Annotation for Modeling and Predicting EC Number Changes in UniProt/Swiss-Prot

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  • Sabrina de Azevedo Silveira
  • Raquel Cardoso de Melo-Minardi
  • Carlos Henrique da Silveira
  • Marcelo Matos Santoro
  • Wagner Meira Jr

Abstract

The volume and diversity of biological data are increasing at very high rates. Vast amounts of protein sequences and structures, protein and genetic interactions and phenotype studies have been produced. The majority of data generated by high-throughput devices is automatically annotated because manually annotating them is not possible. Thus, efficient and precise automatic annotation methods are required to ensure the quality and reliability of both the biological data and associated annotations. We proposed ENZYMatic Annotation Predictor (ENZYMAP), a technique to characterize and predict EC number changes based on annotations from UniProt/Swiss-Prot using a supervised learning approach. We evaluated ENZYMAP experimentally, using test data sets from both UniProt/Swiss-Prot and UniProt/TrEMBL, and showed that predicting EC changes using selected types of annotation is possible. Finally, we compared ENZYMAP and DETECT with respect to their predictions and checked both against the UniProt/Swiss-Prot annotations. ENZYMAP was shown to be more accurate than DETECT, coming closer to the actual changes in UniProt/Swiss-Prot. Our proposal is intended to be an automatic complementary method (that can be used together with other techniques like the ones based on protein sequence and structure) that helps to improve the quality and reliability of enzyme annotations over time, suggesting possible corrections, anticipating annotation changes and propagating the implicit knowledge for the whole dataset.

Suggested Citation

  • Sabrina de Azevedo Silveira & Raquel Cardoso de Melo-Minardi & Carlos Henrique da Silveira & Marcelo Matos Santoro & Wagner Meira Jr, 2014. "ENZYMAP: Exploiting Protein Annotation for Modeling and Predicting EC Number Changes in UniProt/Swiss-Prot," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0089162
    DOI: 10.1371/journal.pone.0089162
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    References listed on IDEAS

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    1. Doug Howe & Maria Costanzo & Petra Fey & Takashi Gojobori & Linda Hannick & Winston Hide & David P. Hill & Renate Kania & Mary Schaeffer & Susan St Pierre & Simon Twigger & Owen White & Seung Yon Rhee, 2008. "The future of biocuration," Nature, Nature, vol. 455(7209), pages 47-50, September.
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    1. Elisa Boari de Lima & Wagner Meira Júnior & Raquel Cardoso de Melo-Minardi, 2016. "Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-32, June.

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