Prediction of Subcellular Localization of Apoptosis Proteins by Dipeptide Composition

By cluster analysis, all dipeptides are classified into 16 categories according to their hydrophobicity, Based on the composition of dipeptide categories, a novel representation of protein sequences is proposed here to predict the subcellular location of apoptosis protein sequences. Using K-Nearest Neighbor Classifier, and test on a known dataset which includes 317 apoptosis proteins , the higher predictive success rates are obtained, the total prediction accuracy of our method is 88.3%. In order to validate the feasibility of the method ulteriorly, we do the same work on a new expanded dataset which includes 1551 apoptosis proteins, the total prediction accuracy is 78.3%, these results indicate that the composition of dipeptide categories combined with K-Nearest Neighbor Classifier is very useful for predicting subcellular location of apoptosis proteins .