Neuropeptides are an important class of cell to cell signaling molecules that are difficult to predict from genetic information because of their large number of post-translational modifications. The transition from prohormone genetic sequence information to the determination of the biologically active neuropeptides requires the identification of the cleaved basic sites, among the many possible cleavage sites, that exist in the prohormone. We report a binary logistic regression model trained on mammalian prohormones that is more sensitive than existing methods in predicting these processing sites, and demonstrate the application of this method to mammalian neuropeptidomic studies. By comparing the predictive abilities of a binary logistic model trained on molluscan prohormone cleavages with the reported model, we establish the need for phyla-specific models.

Original languageEnglish (US)
Pages (from-to)1162-1167
Number of pages6
JournalJournal of Proteome Research
Issue number5
StatePublished - May 2006


  • Binary logistic regression
  • Mammalian prohormones
  • Neuropeptide
  • Prohormone processing prediction
  • Statistical methods

ASJC Scopus subject areas

  • Genetics
  • Biotechnology
  • Biochemistry


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