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Toward a definition of the complete proteome of plant peroxisomes: Where experimental proteomics must be complemented by bioinformatics
Author(s) -
Reumann Sigrun
Publication year - 2011
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201000681
Subject(s) - peroxisome , proteome , biology , proteomics , computational biology , arabidopsis , peroxisomal targeting signal , protein targeting , biochemistry , membrane protein , gene , mutant , membrane
In the past few years, proteome analysis of Arabidopsis peroxisomes has been established by the complementary efforts of four research groups and has emerged as the major unbiased approach to identify new peroxisomal proteins on a large scale. Collectively, more than 100 new candidate proteins from plant peroxisomes have been identified, including long‐awaited low‐abundance proteins. More than 50 proteins have been validated as peroxisome targeted, nearly doubling the number of established plant peroxisomal proteins. Sequence homologies of the new proteins predict unexpected enzyme activities, novel metabolic pathways and unknown non‐metabolic peroxisome functions. Despite this remarkable success, proteome analyses of plant peroxisomes remain highly material intensive and require major preparative efforts. Characterization of the membrane proteome or post‐translational protein modifications poses major technical challenges. New strategies, including quantitative mass spectrometry methods, need to be applied to allow further identifications of plant peroxisomal proteins, such as of stress‐inducible proteins. In the long process of defining the complete proteome of plant peroxisomes, the prediction of peroxisome‐targeted proteins from plant genome sequences emerges as an essential complementary approach to identify additional peroxisomal proteins that are, for instance, specific to peroxisome variants from minor tissues and organs or to abiotically stressed model and crop plants.