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URL:
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http://www.imtech.res.in/raghava/eslpred/
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What you can do:
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Predict the subcellular location of eukaryotic proteins from their different features such as amino acid composition, dipeptide composition and physico-chemical properties.
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Highlights:
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- In order to improve the prediction accuracy, a hybrid module was developed using all features of a protein, which consisted of an input vector of 458 dimensions (400 dipeptide compositions, 33 properties, 20 amino acid compositions of the protein and 5 from PSI-BLAST output).
- Using this hybrid approach, the prediction accuracies of nuclear, cytoplasmic, mitochondrial and extracellular proteins reached 95.3, 85.2, 68.2 and 88.9%, respectively.
- The overall prediction accuracy of SVM modules based on amino acid composition, physico-chemical properties, dipeptide composition and the hybrid approach was 78.1, 77.8, 82.9 and 88.0%, respectively.
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Keywords:
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- protein localizations
- protein localization prediction tool
- protein subcellular localization prediction tool
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Literature and Tutorials:
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PubMed Link: ESLpred
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This record last updated: 05-23-2005
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