title: Enzyme classification with peptide programs: a comparative study.
abstract: BACKGROUND
  Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning methods have been applied to predict protein function, but they lose much of the information encoded by protein sequences because they need to transform them to obtain data of fixed length.
  
  
  RESULTS
  We have developed a machine learning methodology, called peptide programs (PPs), to deal directly with protein sequences and compared its performance with that of Support Vector Machines (SVMs) and BLAST in detailed enzyme classification tasks. Overall, the PPs and SVMs had a similar performance in terms of Matthews Correlation Coefficient, but the PPs had generally a higher precision. BLAST performed globally better than both methodologies, but the PPs had better results than BLAST and SVMs for the smaller datasets.
  
  
  CONCLUSIONS
  The higher precision of the PPs in comparison to the SVMs suggests that dealing with sequences is advantageous for detailed protein classification, as precision is essential to avoid annotation errors. The fact that the PPs performed better than BLAST for the smaller datasets demonstrates the potential of the methodology, but the drop in performance observed for the larger datasets indicates that further development is required.Possible strategies to address this issue include partitioning the datasets into smaller subsets and training individual PPs for each subset, or training several PPs for each dataset and combining them using a bagging strategy.
authors.affiliation: Department of Informatics, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal. dfaria@xldb.di.fc.ul.pt
authors.forename: Daniel
authors.initials: D
authors.lastname: Faria
authors.forename: António E N
authors.initials: AE
authors.lastname: Ferreira
authors.forename: André O
authors.initials: AO
authors.lastname: Falcão
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry*; D010455:Peptides / Q000737:chemistry* / Q000145:classification; D011506:Proteins / Q000145:classification; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D004798:Enzymes; D010455:Peptides; D011506:Proteins