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This article is part of the supplement: The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07)

Open Access Research

Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition

Tanwir Habib1, Chaoyang Zhang2, Jack Y Yang3, Mary Qu Yang4 and Youping Deng1*

Author Affiliations

1 Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA

2 School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA

3 Harvard Medical School, Harvard University, Cambridge, Massachusetts 02140, USA

4 National Human Genome Research Institute, National Institutes of Health (NIH), U.S. Department of Health and Human Services Bethesda, MD 20852, USA

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BMC Genomics 2008, 9(Suppl 1):S16  doi:10.1186/1471-2164-9-S1-S16

Published: 20 March 2008

Abstract

Background

Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy.

Results

We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to predict the subcellular location based on amino acid composition and amino acid pair composition. Results were calculated after 10-fold cross validation. Radial Basis Function (RBF) outperformed polynomial and linear kernel functions. Total prediction accuracy reached to 71.8% for amino acid composition and 77.0% for amino acid pair composition. In order to observe the impact of number of subcellular locations we constructed two more datasets of nine and five subcellular locations. Total accuracy was further improved to 79.9% and 85.66%.

Conclusions

A new SVM based approach is presented based on amino acid and amino acid pair composition. Result shows that data simulation and taking more protein features into consideration improves the accuracy to a great extent. It was also noticed that the data set needs to be crafted to take account of the distribution of data in all the classes.