TARGET: A new method for predicting protein subcellular localization in eukaryotes

Chittibabu Guda, Shankar Subramaniam

Research output: Contribution to journalArticle

92 Citations (Scopus)

Abstract

Motivation: There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species. Results: The nine subcellular locations predicted by pTARGET include cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane and peroxisomes. Predictions are based on the location-specific protein functional domains and the amino acid compositional differences across different subcellular locations. Overall, this method can predict 68-87% of the true positives at accuracy rates of 96-99%. Comparison of the prediction performance against PSORT showed that pTARGET prediction rates are higher by 11-60% in 6 of the 8 locations tested. Besides, the pTARGET method is robust enough for genome-scale prediction of protein subcellular localizations since, it does not rely on the presence of signal or target peptides.

Original languageEnglish (US)
Pages (from-to)3963-3969
Number of pages7
JournalBioinformatics
Volume21
Issue number21
DOIs
StatePublished - Nov 1 2005

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Eukaryota
Proteins
Protein
Target
Prediction
Genome
Genes
Predict
Endoplasmic Reticulum
Peroxisomes
Plasma Membrane
Mitochondria
Performance Prediction
Lysosomes
Peptides
Computational Methods
Nucleus
Cell membranes
Amino Acids
Computational methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

TARGET : A new method for predicting protein subcellular localization in eukaryotes. / Guda, Chittibabu; Subramaniam, Shankar.

In: Bioinformatics, Vol. 21, No. 21, 01.11.2005, p. 3963-3969.

Research output: Contribution to journalArticle

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