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

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.

Figure 1

Bar chart comparing mean true negative fraction (TNf), mean true positive fraction (TPf) and mean total accuracy (TA) for 420 features dataset with SVM using RBF, Polynomial and Linear kernel.

Figure 2

Bar chart displaying total accuracy for 420 features dataset with SVM using RBF kernel.

Figure 3

Bar chart comparing mean true negative fraction (TNf), mean true positive fraction (TPf) and mean total accuracy (TA) for 420 and 20 features dataset with SVM using RBF kernel.

Figure 4

Bar chart comparing TNf, TPf and TA for 420 features dataset using 12, 9 and 5 subcellular locations. SVM is used with Polynomial kernel.

Figure 5

Bar chart comparing TNf, TPf and TA for 420 features dataset with balanced and unbalanced data. SVM is used with Polynomial kernel.

Figure 6

ROC curve analysis for RBF, Polynomial and linear kernel.