Predicting Subcellular Localization of Proteins Based on their Amino Acid Sequence

Olof Emanuelsson
Stockholm Bioinformatics Center
Stockholm University
telephone: +46 (0)8 16 21 10
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presenter: Olof Emanuelsson

Olof Emanuelsson1, Henrik Nielsen2, Gunnar von Heijne1
1Stockholm Bioinformatics Center, Stockholm University, Stockholm, Sweden
2Center for Biological Sequence Analysis, The Technical University of Denmark, Lyngby, Denmark

The subcellular localization of a protein is an important characteristic with implication for protein function. We have developed TargetP, which is a neural network-based tool for large-scale subcellular location prediction of newly identified proteins. TargetP is able to discriminate between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and "other" localizations with a succes rate of 85% (plant) or 90% (non-plant) on redundancy-reduced test sets. Scanning the newly sequenced A. thaliana chromosomes 2 and 4 and the Ensembl human set with TargetP, we estimate that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%. A recent extension of TargetP is a peroxisomal prediction module that at a well above-random rate is able to tell apart peroxisomal proteins with a C-terminal targeting tripeptide (PTS1, 22 different motifs found) from non-peroxisomal proteins whose three C-terminal residues coincide with any of the accepted PTS1 motifs. Current research also focus on increasing the prediction accuracy by incorporating multiple alignment and secondary structure information, and on further testing of prediction ability as more and more sequences become available. TargetP is found at

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