Display options
Cite
Patrick R, Cao KA, Davis M, et al. Mapping the stabilome: a novel computational method for classifying metabolic protein stability. BMC Syst Biol. 2012;6:60doi: 10.1186/1752-0509-6-60.
Patrick, R., Cao, K. A., Davis, M., Kobe, B., & Bodén, M. (2012). Mapping the stabilome: a novel computational method for classifying metabolic protein stability. BMC systems biology, 660. https://doi.org/10.1186/1752-0509-6-60
Patrick, Ralph, et al. "Mapping the stabilome: a novel computational method for classifying metabolic protein stability." BMC systems biology vol. 6 (2012): 60. doi: https://doi.org/10.1186/1752-0509-6-60
Patrick R, Cao KA, Davis M, Kobe B, Bodén M. Mapping the stabilome: a novel computational method for classifying metabolic protein stability. BMC Syst Biol. 2012 Jun 08;6:60. doi: 10.1186/1752-0509-6-60. PMID: 22682214; PMCID: PMC3439251.
Copy Download .nbib Format: NLM
Share it on

BMC Syst Biol. 2012 Jun 08;6:60. doi: 10.1186/1752-0509-6-60.

Mapping the stabilome: a novel computational method for classifying metabolic protein stability.

BMC systems biology

Ralph Patrick, Kim-Anh Lê Cao, Melissa Davis, Bostjan Kobe, Mikael Bodén

Affiliations

  1. School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Australia.

PMID: 22682214 PMCID: PMC3439251 DOI: 10.1186/1752-0509-6-60

Abstract

BACKGROUND: The half-life of a protein is regulated by a range of system properties, including the abundance of components of the degradative machinery and protein modifiers. It is also influenced by protein-specific properties, such as a protein's structural make-up and interaction partners. New experimental techniques coupled with powerful data integration methods now enable us to not only investigate what features govern protein stability in general, but also to build models that identify what properties determine each protein's metabolic stability.

RESULTS: In this work we present five groups of features useful for predicting protein stability: (1) post-translational modifications, (2) domain types, (3) structural disorder, (4) the identity of a protein's N-terminal residue and (5) amino acid sequence. We incorporate these features into a predictive model with promising accuracy. At a 20% false positive rate, the model exhibits an 80% true positive rate, outperforming the only previously proposed stability predictor. We also investigate the impact of N-terminal protein tagging as used to generate the data set, in particular the impact it may have on the measurements for secreted and transmembrane proteins; we train and test our model on a subset of the data with those proteins removed, and show that the model sustains high accuracy. Finally, we estimate system-wide metabolic stability by surveying the whole human proteome.

CONCLUSIONS: We describe a variety of protein features that are significantly over- or under-represented in stable and unstable proteins, including phosphorylation, acetylation and destabilizing N-terminal residues. Bayesian networks are ideal for combining these features into a predictive model with superior accuracy and transparency compared to the only other proposed stability predictor. Furthermore, our stability predictions of the human proteome will find application in the analysis of functionally related proteins, shedding new light on regulation by protein synthesis and degradation.

References

  1. Science. 1991 May 3;252(5006):668-74 - PubMed
  2. Genes Cells. 1997 Jan;2(1):13-28 - PubMed
  3. Nucleic Acids Res. 2011 Jan;39(Database issue):D261-7 - PubMed
  4. In Silico Biol. 2006;6(5):387-99 - PubMed
  5. Mol Syst Biol. 2010 Aug 24;6:400 - PubMed
  6. Trends Cell Biol. 2009 Nov;19(11):649-55 - PubMed
  7. Trends Cell Biol. 2011 May;21(5):293-303 - PubMed
  8. J Biochem. 2003 Aug;134(2):183-90 - PubMed
  9. Bioinformatics. 2011 May 1;27(9):1239-46 - PubMed
  10. Cell Biol Int. 2011 May;35(5):457-62 - PubMed
  11. IEEE Trans Pattern Anal Mach Intell. 2005 Aug;27(8):1226-38 - PubMed
  12. Science. 2010 Feb 19;327(5968):973-7 - PubMed
  13. Structure. 2003 Nov;11(11):1453-9 - PubMed
  14. PLoS One. 2010 Jun 04;5(6):e10972 - PubMed
  15. Proc Natl Acad Sci U S A. 2002 May 14;99(10):6562-6 - PubMed
  16. Pac Symp Biocomput. 2002;:564-75 - PubMed
  17. Proc Natl Acad Sci U S A. 2006 Aug 29;103(35):13004-9 - PubMed
  18. Nat Protoc. 2007;2(4):953-71 - PubMed
  19. J Chem Inf Model. 2008 Apr;48(4):785-96 - PubMed
  20. EMBO J. 1998 Jun 15;17(12):3251-7 - PubMed
  21. Bioinformatics. 2011 Jul 1;27(13):i7-14 - PubMed
  22. J Proteome Res. 2009 Jan;8(1):104-12 - PubMed
  23. Nat Biotechnol. 2007 Mar;25(3):285-6 - PubMed
  24. Methods Mol Biol. 2009;577:67-79 - PubMed
  25. Nat Rev Mol Cell Biol. 2008 Sep;9(9):679-90 - PubMed
  26. Nat Biotechnol. 2008 Aug;26(8):897-9 - PubMed
  27. Trends Cell Biol. 2007 Apr;17(4):165-72 - PubMed
  28. Science. 2008 Nov 7;322(5903):918-23 - PubMed
  29. Genome Biol. 2009;10(5):R50 - PubMed
  30. Bioinformatics. 2000 May;16(5):412-24 - PubMed
  31. Mol Cell. 2007 Dec 14;28(5):730-8 - PubMed
  32. Science. 1986 Oct 17;234(4774):364-8 - PubMed
  33. Proteins. 2008 May 1;71(2):903-9 - PubMed
  34. Science. 2011 Feb 11;331(6018):764-8 - PubMed
  35. Annu Rev Genet. 1996;30:405-39 - PubMed

Substances

MeSH terms

Publication Types