title: ENZYMAP: exploiting protein annotation for modeling and predicting EC number changes in UniProt/Swiss-Prot.
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.
authors.affiliation: Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil ; Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
authors.forename: Sabrina de Azevedo
authors.initials: Sde A
authors.lastname: Silveira
authors.affiliation: Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
authors.forename: Raquel Cardoso
authors.initials: RC
authors.lastname: de Melo-Minardi
authors.affiliation: Department of Biotechnology, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil.
authors.forename: Carlos Henrique
authors.initials: CH
authors.lastname: da Silveira
authors.affiliation: Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
authors.forename: Marcelo Matos
authors.initials: MM
authors.lastname: Santoro
authors.affiliation: Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
authors.forename: Wagner
authors.initials: W
authors.lastname: Meira
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism; D005544:Forecasting; D006801:Humans; D008958:Models, Molecular; D058977:Molecular Sequence Annotation / Q000379:methods*; D017434:Protein Structure, Tertiary; D012984:Software
chemical_list: D004798:Enzymes