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Updated: May 13, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
PeptideLocator: prediction of bioactive peptides in protein sequences
Catherine Mooney1, Niall J Haslam, Thérèse A Holton
1Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland.
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.
Availability And Implementation:
PeptideLocator is freely available for academic users at http://bioware.ucd.ie/.
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