期刊
PROTEOMICS
卷 10, 期 23, 页码 4213-4219出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/pmic.201000359
关键词
Bioinformatics; Organelle proteomics; Protein localisation; Statistical models; Support vector machines
资金
- EU framework 6 WallNet Consortium
- BBSRC [BB/E024777/1]
- MRC Centre for Stern Cell Biology and Regenerative Medicine University of Cambridge
- BBSRC [BB/E024777/1] Funding Source: UKRI
- MRC [G0800784] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/E024777/1] Funding Source: researchfish
- Medical Research Council [G0800784B, G0800784] Funding Source: researchfish
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 protei n-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
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