PredPlantPTS1: A Web Server for the Prediction of Plant Peroxisomal Proteins.

ABSTRACT: Prediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal proteins carrying the peroxisome targeting signal type 1 (PTS1). However, plant-specific PTS1 protein prediction methods have been lacking up to now, and pre-existing methods generally were incapable of correctly predicting low-abundance plant proteins possessing non-canonical PTS1 patterns. Recently, we presented a machine learning approach that is able to predict PTS1 proteins for higher plants (spermatophytes) with high accuracy and which can correctly identify unknown targeting patterns, i.e., no

SUBMITTER: Reumann S 

PROVIDER: S-EPMC3427985 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature