Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Trotter, Matthew W. B., Sadowski, Pawel G., Dunkley, Tom P. J., Groen, Arnoud J., & Lilley, Kathryn S. (2010) Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions. Proteomics, 10(23), pp. 4213-4219.

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Abstract

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Impact and interest:

20 citations in Scopus
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19 citations in Web of Science®

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ID Code: 65597
Item Type: Journal Article
Refereed: Yes
Keywords: Bioinformatics, Organelle proteomics, Protein localisation, Statistical models, Support vector machines
DOI: 10.1002/pmic.201000359
ISSN: 1615-9861
Divisions: Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 02 Jan 2014 01:48
Last Modified: 18 Feb 2014 07:36

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