Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.
Subcellular localization is an important aspect of protein function, but the protein composition of many intracellular compartments is poorly characterized. For example, many nuclear bodies are challenging to isolate biochemically and thus remain inaccessible to proteomics. Here, we explore covariation in proteomics data as an alternative route to subcellular proteomes. Rather than targeting a structure of interest biochemically, we target it by machine learning. This becomes possible by taking data obtained for one organelle and searching it for traces of another organelle. As an extreme example and proof-of-concept we predict mitochondrial proteins based on their covariation in published interphase chromatin data. We detect about ⅓ of the known mitochondrial proteins in our chromatin data, presumably most as contaminants. However, these proteins are not present at random. We show covariation of mitochondrial proteins in chromatin proteomics data. We then exploit this covariation by multiclassifier combinatorial proteomics to define a list of mitochondrial proteins. This list agrees well with different databases on mitochondrial composition. This benchmark test raises the possibility that, in principle, covariation proteomics may also be applicable to structures for which no biochemical isolation procedures are available.
Wellcome Trust Centre for Cell Biology, University of Edinburgh, UK.
Georg
G
Kustatscher
Wellcome Trust Centre for Cell Biology, University of Edinburgh, UK.|Department of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.
Piotr
P
Grabowski
Wellcome Trust Centre for Cell Biology, University of Edinburgh, UK.|Department of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.
Juri
J
Rappsilber
D002467:Cell Nucleus / Q000737:chemistry* / Q000378:metabolism; D002843:Chromatin / Q000737:chemistry* / Q000378:metabolism; D057225:Data Mining; D030562:Databases, Protein; D057809:HEK293 Cells; D006367:HeLa Cells; D056945:Hep G2 Cells; D006801:Humans; D007553:Isotope Labeling; D061986:MCF-7 Cells; D000069550:Machine Learning; D008928:Mitochondria / Q000737:chemistry* / Q000378:metabolism; D024101:Mitochondrial Proteins / Q000737:chemistry / Q000302:isolation & purification*; D020543:Proteome / Q000737:chemistry / Q000302:isolation & purification*; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve
D002843:Chromatin; D024101:Mitochondrial Proteins; D020543:Proteome
Chromatin; Machine learning; Mitochondria; Organelle; Systems biology