Mammalian Genome

, Volume 19, Issue 2, pp 106–120 | Cite as

Comparative analysis of neuropeptide cleavage sites in human, mouse, rat, and cattle

  • Allison N. Tegge
  • Bruce R. Southey
  • Jonathan V. Sweedler
  • Sandra L. Rodriguez-ZasEmail author


Neuropeptides are an important class of signaling molecules that result from complex and variable posttranslational processing of precursor proteins and thus are difficult to identify based solely on genomic information. Bioinformatics prediction of precursor cleavage sites can support effective biochemical characterization of neuropeptides. Neuropeptide cleavage models were developed using comprehensive human, mouse, rat, and cattle precursor data sets and used to compare predicted neuropeptide processing across these species. Logistic regression and artificial neural network models were used to predict cleavages based on amino acid and physiochemical properties of amino acids at precursor sequence locations proximal to cleavage. Correct cleavage classification rates across species and models ranged from 85% to 100%, suggesting that amino acid and amino acid properties have major impact on the probability of cleavage and that these factors have comparable effects in human, mouse, rat, and cattle. The variable accuracy of each species-specific model to predict cleavage sites indicated that there are species- and precursor-specific processing patterns. Prediction of mouse cleavages using rat models was highly accurate, yet the reverse was not observed. Sensitivity and specificity revealed that logistic models are well suited to maximize the rate of true noncleavage predictions with moderate rates of true cleavage predictions; meanwhile, artificial neural networks maximize the rate of true cleavage predictions with moderate to low true noncleavage predictions. Logistic models also provided insights into the strength of the amino acid associations with cleavage. Prediction of neuropeptide cleavage sites using human, mouse, rat, and cattle models are available at


Artificial Neural Network Artificial Neural Network Model Correct Classification Rate Mammalian Model Amino Acid Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The financial support of NIH/NIGMS under 5R01GM068946 and the National Institute on Drug Abuse under Award No. P30 DA 018310 to the UIUC Neuroproteomics Center is highly appreciated.

Supplementary material

335_2007_9090_MOESM1_ESM.doc (83 kb)
(DOC 83 kb)


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Allison N. Tegge
    • 1
  • Bruce R. Southey
    • 2
    • 3
  • Jonathan V. Sweedler
    • 2
    • 4
  • Sandra L. Rodriguez-Zas
    • 1
    • 4
    • 5
    Email author
  1. 1.Department of Animal SciencesUniversity of IllinoisUrbanaUSA
  2. 2.Department of ChemistryUniversity of IllinoisUrbanaUSA
  3. 3.Department of Computer ScienceUniversity of IllinoisUrbanaUSA
  4. 4.Institute of Genomic BiologyUniversity of IllinoisUrbanaUSA
  5. 5.Department of StatisticsUniversity of IllinoisUrbanaUSA

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