Protein Subcellular Location Prediction Based on Pseudo Amino Acid Composition and PSI-Blast Profile
The location of a protein in a cell is closely correlated with its biological function. At present, it is still a challenge to extract sequence information solely using protein sequence for protein subcellular localization prediction in the current computational biology. In the current paper, we proposed a novel method that coupled the amino acid composition, amino acid hydrophobicity with position specific scoring matrix to predict the subcellular localizations of prokaryotic and eukaryotic proteins. By defined evolutionary difference formula, varying length proteins are expressed as uniform dimensional vectors, which can represent evolutionary difference information between the u residues of a given protein. To perform and evaluate the proposed method, support vector machine (SVM) and jackknife tests are employed. The overall accuracies are 95.1% and 90.9% for prokaryotic and eukaryotic proteins, respectively. Comparison of our results with the previous methods shows that our method may provide a promising method to predict protein subcellular localization prediction.
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Document Type: Research Article
Publication date: October 1, 2015
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- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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