Skip to main content

Advertisement

Log in

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

  • Published:
Mammalian Genome Aims and scope Submit manuscript

Abstract

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 http://www.neuroproteomics.scs.uiuc.edu/neuropred.html.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Agresti A (1996) An Introduction to Categorical Data Analysis. New York: John Wiley and Sons

    Google Scholar 

  • Amare A, Hummon AB, Southey BR, Zimmerman TA, Rodriguez-Zas SL, et al. (2006) Bridging neuropeptidomics and genomics with bioinformatics: prediction of mammalian neuropeptide prohormone processing. J Proteome Res 5(5):1162–1167

    Article  PubMed  CAS  Google Scholar 

  • Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16(5):412–424

    Article  PubMed  CAS  Google Scholar 

  • Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340(4):783–795

    Article  PubMed  Google Scholar 

  • Berg JM, Tymoczko JL, Stryer L (2002) Biochemistry, 5th ed. (New York: WH Freeman)

    Google Scholar 

  • Beinfeld MC (2003) Biosynthesis and processing of pro CCK: recent progress and future challenges. Life Sci 72(7):747–757

    Article  PubMed  CAS  Google Scholar 

  • Dey A, Lipkind GM, Rouillé Y, Norrbom C, Stein J, et al. (2005) Significance of prohormone convertase 2, PC2, mediated initial cleavage at the proglucagon interdomain site, Lys70-Arg71, to generate glucagons. Endocrinology 146(2):713–727

    Article  PubMed  CAS  Google Scholar 

  • Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35(5/6):352–359

    Article  PubMed  Google Scholar 

  • Duckert P, Brunak S, Blom N (2004) Prediction of proprotein convertase cleavage sites. Protein Eng Des Sel 17(1):107–112

    Article  PubMed  CAS  Google Scholar 

  • Francis L (2001) Neural networks demystified. Casualty actuarial society forum. Casualty Actuarial Society. Winter 2001:253–320

    Google Scholar 

  • Fricker LD (2005) Neuropeptide-processing enzymes: applications for drug discovery. AAPS J 7(2):E449–455

    Article  PubMed  CAS  Google Scholar 

  • Henrich S, Cameron A, Bourenkov GP, Kiefersauer R, Huber R, et al. (2003) The crystal structure of the proprotein processing proteinase furin explains its stringent specificity. Nat Struct Biol 10(7):520–526

    Article  PubMed  CAS  Google Scholar 

  • Hinuma S, Shintani Y, Fukusumi S, Iijima N, Matsumoto Y, et al. (2000) New neuropeptides containing carboxy-terminal RFamide and their receptor in mammals. Nat Cell Biol 2(10):703–708

    Article  PubMed  CAS  Google Scholar 

  • Holyoak T, Wilson MA, Fenn TD, Kettner CA, Petsko GA, et al. (2003) 2.4 Å Resolution crystal structure of the prototypical hormone-processing protease Kex2 in complex with an Ala-Lys-Arg boronic acid inhibitor. Biochemistry 42(22):6709–6718

    Article  PubMed  CAS  Google Scholar 

  • Hook VY (2006) Unique neuronal functions of cathepsin L and cathepsin B in secretory vesicles: biosynthesis of peptides in neurotransmission and neurodegenerative disease. Biol Chem 387(10–11):1429–1439

    Article  PubMed  CAS  Google Scholar 

  • Hummon AB, Hummon NP, Corbin RW, Li LJ, Vilim FS, et al. (2003) From precursor to final peptides: a statistical sequence-based approach to predicting prohormone processing. J Proteome Res 2(6):650–656

    Article  PubMed  CAS  Google Scholar 

  • Hummon AB, Amare A, Sweedler JV (2006) Discovering new invertebrate neuropeptides using mass spectrometry. Mass Spectrom Rev 25(1):77–98

    Article  PubMed  CAS  Google Scholar 

  • Jiang Y, Luo L, Gustafson EL, Yadav D, Laverty M, et al. (2003) Identification and characterization of a novel RF-amide peptide ligand for orphan G-protein-coupled receptor SP9155. J Biol Chem 278(30):27652–27657

    Article  PubMed  CAS  Google Scholar 

  • Larkin DM, Astakhova NM, Prokhorovich MA, Lewin HA, Zhdanova NS (2006) Comparative mapping of cattle chromosome 19: cytogenetic localization of 19 BAC clones. Cytogenet Genome Res 112(3–4):235–240

    Article  PubMed  CAS  Google Scholar 

  • Matthews BW (1975) Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405(2):442–451

    PubMed  CAS  Google Scholar 

  • Reed Murphy L, Wallqvist A, Levy RM (2000) Simplified amino acid alphabets for protein fold recognition and implications for folding. Protein Eng 13(3):149–152

    Article  Google Scholar 

  • Rockwell NC, Krysan DJ, Komiyama T, Fuller RS (2002) Precursor processing by kex2/furin proteases. Chem Rev 102(12):4525–4548

    Article  PubMed  CAS  Google Scholar 

  • Scamuffa N, Calvo F, Chretien M, Seidah NG, Khatib AM (2006) Proprotein convertases: lessons from knockouts. FASEB J 20(12):1954–1963

    Article  PubMed  CAS  Google Scholar 

  • Schechter I, Berger A (1967) On the size of the active site in proteases. I. Papain. Biochem Biophys Res Commun 27(2):157–162

    Article  CAS  Google Scholar 

  • Southey BR, Amare A, Zimmerman TA, Rodriguez-Zas SL, Sweedler JV (2006a) NeuroPred: a tool to predict cleavage sites in neuropeptide precursors and provide the masses of the resulting peptides. Nucleic Acids Res 34(Web Server issue):W267–272

    Article  PubMed  CAS  Google Scholar 

  • Southey BR, Rodriguez-Zas SL, Sweedler JV (2006b) Prediction of neuropeptide prohormone cleavages with application to RFamides. Peptides 27(5):1087–1098

    Article  PubMed  CAS  Google Scholar 

  • Tegge AN, Rodriguez-Zas SL, Sweedler JV, Southey BR (2007) Enhanced prediction of cleavage in bovine precursor sequences. In: Bioinformatics Research and Applications, Third International Symposium, ISBRA 2007, Atlanta, GA, USA, May 7–10, 2007, Proceedings, Lecture Notes in Computer Science 4463, Mandoiu I, Zelikovsky A (eds.) (New York: Springer–Verlag), pp 350–360

  • The UniProt Consortium (2007) The Universal Protein Resource (UniProt). Nucleic Acids Res 35(35):D193–197

    Article  Google Scholar 

  • Thomas G, Thorne BA, Thomas L, Allen RG, Hruby DE, et al. (1988) Yeast KEX2 endopeptidase correctly cleaves a neuroendocrine prohormone in mammalian cells. Science 241(4862):226–230

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sandra L. Rodriguez-Zas.

Additional information

Allison Tegge and Bruce Southey contributed equally to this work.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tegge, A.N., Southey, B.R., Sweedler, J.V. et al. Comparative analysis of neuropeptide cleavage sites in human, mouse, rat, and cattle. Mamm Genome 19, 106–120 (2008). https://doi.org/10.1007/s00335-007-9090-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00335-007-9090-9

Keywords

Navigation