PeptideLocator: prediction of bioactive peptides in protein sequences

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Title: PeptideLocator: prediction of bioactive peptides in protein sequences
Authors: Mooney, Catherine
Haslam, Niall J.
Holton, Thérèse A.
Pollastri, Gianluca
Shields, Denis C.
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Date: 1-May-2013
Online since: 2019-04-24T11:07:34Z
Abstract: Motivation: 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.
Funding Details: Enterprise Ireland
Science Foundation Ireland
Type of material: Journal Article
Publisher: Oxford University Press
Journal: Bioinformatics
Volume: 29
Issue: 9
Start page: 1120
End page: 1126
Copyright (published version): 2013 the Authors
Keywords: PeptidesAntimicrobialCationic peptidesArtificial intelligenceProteinSequence analysisAlgorithms
DOI: 10.1093/bioinformatics/btt103
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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