Please use this identifier to cite or link to this item:
Main Title: Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data
Author(s): Kustatscher, Georg
Grabowski, Piotr
Rappsilber, Juri
Type: Article
Language Code: en
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 1/3 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.
Issue Date: 2016
Date Available: 14-Jul-2017
DDC Class: 540 Chemie und zugeordnete Wissenschaften
Subject(s): chromatin
machine learning
systems biology
Journal Title: Proteomics
Publisher: Wiley-VCH
Publisher Place: Weinheim
Volume: 16
Issue: 3
Publisher DOI: 10.1002/pmic.201500267
Page Start: 393
Page End: 401
EISSN: 1615-9861
ISSN: 1615-9853
Appears in Collections:FG Bioanalytik » Publications

Files in This Item:
File Description SizeFormat 
10.1002.pmic.201500267.pdf688.36 kBAdobe PDFThumbnail

This item is licensed under a Creative Commons License Creative Commons