title: PeptideLocator: prediction of bioactive peptides in protein sequences.
abstract: BACKGROUND
  Peptides play important roles in signalling, regulation and immunity within an organism. Many have successfully been used as therapeutic products often mimicking naturally occurring peptides. Here we present PeptideLocator for the automated prediction of functional peptides in a protein sequence.
  
  
  RESULTS
  We have trained a machine learning algorithm to predict bioactive peptides within protein sequences. PeptideLocator performs well on training data achieving an area under the curve of 0.92 when tested in 5-fold cross-validation on a set of 2202 redundancy reduced peptide containing protein sequences. It has predictive power when applied to antimicrobial peptides, cytokines, growth factors, peptide hormones, toxins, venoms and other peptides. It can be applied to refine the choice of experimental investigations in functional studies of proteins.
  
  
  METHODS
  PeptideLocator is freely available for academic users at http://bioware.ucd.ie/.
authors.affiliation: Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland.
authors.forename: Catherine
authors.initials: C
authors.lastname: Mooney
authors.forename: Niall J
authors.initials: NJ
authors.lastname: Haslam
authors.forename: Thérèse A
authors.initials: TA
authors.lastname: Holton
authors.forename: Gianluca
authors.initials: G
authors.lastname: Pollastri
authors.forename: Denis C
authors.initials: DC
authors.lastname: Shields
mesh_terms: D000465:Algorithms; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry; D001185:Artificial Intelligence; D010455:Peptides / Q000737:chemistry* / Q000145:classification; D011506:Proteins / Q000737:chemistry; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D023181:Antimicrobial Cationic Peptides; D010455:Peptides; D011506:Proteins