Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

Proteome analysis of Arabidopsis leaf peroxisomes reveals novel targeting peptides, metabolic pathways, and defense mechanisms.

We have established a protocol for the isolation of highly purified peroxisomes from mature Arabidopsis thaliana leaves and analyzed the proteome by complementary gel-based and gel-free approaches. Seventy-eight nonredundant proteins were identified, of which 42 novel proteins had previously not been associated with plant peroxisomes. Seventeen novel proteins carried predicted peroxisomal targeting signals (PTS) type 1 or type 2; 11 proteins contained PTS-related peptides. Peroxisome targeting was supported for many novel proteins by in silico analyses and confirmed for 11 representative full-length fusion proteins by fluorescence microscopy. The targeting function of predicted and unpredicted signals was investigated and SSL>, SSI>, and ASL> were established as novel functional PTS1 peptides. In contrast with the generally accepted confinement of PTS2 peptides to the N-terminal domain, the bifunctional transthyretin-like protein was demonstrated to carry internally a functional PTS2. The novel enzymes include numerous enoyl-CoA hydratases, short-chain dehydrogenases, and several enzymes involved in NADP and glutathione metabolism. Seven proteins, including beta-glucosidases and myrosinases, support the currently emerging evidence for an important role of leaf peroxisomes in defense against pathogens and herbivores. The data provide new insights into the biology of plant peroxisomes and improve the prediction accuracy of peroxisome-targeted proteins from genome sequences.