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Open Access Research article

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

Ralph Patrick1, Kim-Anh Lê Cao2, Melissa Davis3, Bostjan Kobe134 and Mikael Bodén13*

Author Affiliations

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

2 Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia, Australia

3 Institute for Molecular Bioscience, The University of Queensland, St Lucia, Australia

4 Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia, Australia

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BMC Systems Biology 2012, 6:60  doi:10.1186/1752-0509-6-60

Published: 8 June 2012

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.

Keywords:
Protein stability; Degradation; Machine learning; Post-translational modifications; Bayesian networks; Support vector machines; Prediction