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Prediction of Subcellular Location of Apoptosis Proteins Using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation

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Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Thus, annotating the function of apoptosis proteins is of significant value. Since the function of apoptosis proteins correlates with their subcellular location, the information about their subcellular location can be very useful in understanding their role in the process of programmed cell death. In the present study, we propose a novel sequence representation that incorporates the evolution information represented in the position-specific score matrices by the auto covariance transformation. Then the support vector machine classifier is adopted to predict subcellular location of apoptosis proteins. To verify the performance of this method, jackknife cross-validation tests are performed on three widely used benchmark datasets and results show that our approach achieves relatively high prediction accuracies over some classical methods.

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Keywords: Apoptosis protein; auto covariance transformation; jackknife cross-validation test; position-specific score matrix; subcellular location; support vector machine

Document Type: Research Article

Publication date: 2010-10-01

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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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