Neuropeptides and hormones are signaling molecules that support cell-cell communication in the central nervous system. Experimentally characterizing neuropeptides requires signifi cant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and fl exibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the fi nal bioactive peptides. We identifi ed 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classifi cation rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifi cations, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an allinclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identifi cation of potential bioactive neuropeptides.

Original languageEnglish (US)
Article number7
JournalFrontiers in Neuroinformatics
Issue numberDEC
StatePublished - Dec 16 2008


  • Bioinformatics
  • Machine learning
  • Neuropeptides
  • Precursor cleavage
  • Python
  • Rhesus monkey
  • Support vector machine

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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