Identification of Novel Plant Peroxisomal Targeting Signals by a Combination of Machine Learning Methods and in Vivo Subcellular Targeting Analyses

Abstract

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 lowabundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

Plant Peroxisomes: Biogenesis and Function

“…This prediction method (i.e., the position weight matrices model) predicts 389 Arabidopsis gene models to encode peroxisomal PTS1 protein variants; ;70% of them are not known to be peroxisomal. Some confirmed peroxisomal PTS1 proteins are located in a gray zone below the prediction threshold, indicating that the number of Arabidopsis peroxisomal proteins might exceed 400 to 500 (Lingner et al, 2011).…”

“…Hence, among many proteins with the same noncanonical PTS1 tripeptide, only a few are indeed peroxisome targeted, and correct computational predictions are difficult. For instance, prediction tools developed for metazoa generally fail to correctly predict plant peroxisomal proteins with noncanonical PTS1 tripeptides (Lingner et al, 2011). The accuracy of prediction algorithms relies on the size, quality, and diversity of the underlying data set of example sequences that is used for model training and limited preexisting prediction algorithms (Emanuelsson et al, 2003;Bodén and Hawkins, 2005;Hawkins et al, 2007).…”

“…Prediction methods, such as PeroxiP and the PTS1 predictor, and databases, such as PeroxisomeDB, were developed to predict and assemble PTS1 proteins from primarily metazoan genomic sequences (see references in Lingner et al, 2011). However, high-accuracy prediction tools have long been lacking for plants.…”

Peroxisome Function, Biogenesis, and Dynamics in Plants

“…Two types of peroxisome targeting signals (PTS) specify matrix protein localization. Most matrix proteins carry a PTS1, a C-terminal SKL or similar tripeptide (Reumann, 2004;Lingner et al, 2011). Fewer proteins carry the PTS2 nonapeptide, often R[L/I]X 5 HL in plants, near the N terminus (Reumann, 2004).…”

The Peroxisomal NAD Carrier from Arabidopsis Imports NAD in Exchange with AMP

“…The counter-exchange substrate for the NAD import might be generated via the hydrolysis of NADH to AMP catalyzed by the NADH pyrophophatase NUDT19 (At5g20070) in Arabidopsis. This member of the NUDIX hydrolase family exhibits activity toward NADH and was shown to be targeted to peroxisomes (Ogawa et al, 2008;Lingner et al, 2011). An additional transport scenario for PXN is the export of CoA (Fig.…”