Predicting Subcellular Localization of Mycobacterial Proteins by Using Chous Pseudo Amino Acid Composition
The successful prediction of protein subcellular localization directly from protein primary sequence is useful to protein function prediction and drug discovery. In this paper, by using the concept of pseudo amino acid composition (PseAAC), the mycobacterial proteins are studied and predicted by support vector machine (SVM) and increment of diversity combined with modified Mahalanobis Discriminant (IDQD). The results of jackknife cross-validation for 450 nonredundant proteins show that the overall predicted successful rates of SVM and IDQD are 82.2% and 79.1%, respectively. Compared with other existing methods, SVM combined with PseAAC display higher accuracies.
Keywords: Protein subcellular localization, pseudo amino acid composition, support vector machine, increment of diversity, modified Mahalanobis Discriminant, Chou's invariance theorem
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