Neuropeptides are important signaling molecules that influence a wide variety of biological processes. The prediction of neuropeptides from precursor proteins is difficult due to the numerous and complex series of enzymatic processing and posttranslational modification steps. Bioinformatics prediction of cleavage sites using statistical models was used to overcome the challenge of identifying neuropeptides. Binary logistic models were trained on a bovine dataset and validated on a mammalian dataset that contained no bovine precursors. A model that incorporated amino acid locations and properties provided more accurate and precise cleavage predictions than one using amino acid locations alone. All models consistently resulted in highly accurate predictions of cleavage sites in both datasets. The logistic model proposed can accurately predict cleavage sites in mammalian species and minimize the time consuming and costly experimental validation of neuropeptides.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings
Number of pages11
ISBN (Print)3540720308, 9783540720300
StatePublished - 2007
Event3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 - Atlanta, GA, United States
Duration: May 7 2007May 10 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4463 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
Country/TerritoryUnited States
CityAtlanta, GA


  • Cleavage
  • Logistic regression
  • Neuropeptide
  • Precursor

ASJC Scopus subject areas

  • General Computer Science
  • General Biochemistry, Genetics and Molecular Biology
  • Theoretical Computer Science


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