Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.

ABSTRACT: 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 dat

SUBMITTER: Kustatscher G 

PROVIDER: S-EPMC4862026 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature