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Predicting Apoptosis Protein Subcellular Location with PseAAC by Incorporating Tripeptide Composition

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The function of the protein is closely correlated with its subcellular localization. Probing into the mechanism of protein sorting and predicting protein subcellular location can provide important clues or insights for understanding the function of proteins. In this paper, we introduce a new PseAAC approach to encode the protein sequence based on the physicochemical properties of amino acid residues. Each of the protein samples was defined as a 146D (dimensional) vector including the 20 amino acid composition components and 126 adjacent triune residues contents. To evaluate the effectiveness of this encoding scheme, we did jackknife tests on three datasets using the support vector machine algorithm. The total prediction accuracies are 84.9%, 91.2%, and 92.6%, respectively. The satisfactory results indicate that our method could be a useful tool in the area of bioinformatics and proteomics.

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Keywords: AAC; Combined feature vector; Cross-validation test; ID_SVM method; PSORT; PseAAC; Pseudo amino acid composition; bioinformatics; hydrophobic; jackknife tests; protein function; proteomics; support vector machine; tripeptide composition

Document Type: Research Article

Publication date: November 1, 2011

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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