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

Patrick, Ralph, Cao, Kim-Anh L., Davis, Melissa, Kobe, Bostjan and Bodén, Mikael (2012) Mapping the stabilome: a novel computational method for classifying metabolic protein stability. BMC Systems Biology, 6 60.1-60.15. doi:10.1186/1752-0509-6-60

Author Patrick, Ralph
Cao, Kim-Anh L.
Davis, Melissa
Kobe, Bostjan
Bodén, Mikael
Title Mapping the stabilome: a novel computational method for classifying metabolic protein stability
Journal name BMC Systems Biology   Check publisher's open access policy
ISSN 1752-0509
Publication date 2012-06
Sub-type Article (original research)
DOI 10.1186/1752-0509-6-60
Open Access Status DOI
Volume 6
Start page 60.1
End page 60.15
Total pages 15
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2013
Language eng
Formatted 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.
Keyword Protein stability
Machine learning
Post-translational modifications
Bayesian networks
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article # 60

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Chemistry and Molecular Biosciences
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 12 Oct 2012, 10:58:39 EST by Mrs Louise Nimwegen on behalf of School of Chemistry & Molecular Biosciences