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Subcellular location of protein is crucial for the dynamic life of cells as it is an important step towards elucidating its function. It is widely recognized that the information from the amino acid sequence can serve as vital pointers in predicting location of proteins. We introduce a new feature vector for predicting proteins targeted to various compartments in the hierarchical structure of cellular sorting pathway from protein sequence. Features are based on the overall Composition, Transition and Distribution (CTD) of amino acid attributes such as hydrophobicity, normalized van der Waals volume, polarity, polarizability, charge, secondary structure and solvent accessibility of the protein sequences. Classification of protein locations in cellular sorting pathway is achieved through Support Vector Machine. Our method gives an accuracy of 92% in human and 95% in fungi with non redundant test set at root level.