A Novel Pseudo Amino Acid Composition for Predicting Subcellular Location of Proteins

Information on subcellular localization of proteins plays a vitally important role in molecular cell biology, proteomics and drug discovery. In this field, finding the most suitable representation for protein sample is one of the most crucial procedures. Inspired by the modes of pseudo amino acid composition (PAA), cellular automaton image (CAI) for protein and the chaos game representation (CGR) for DNA sequence, a 20-dimension CGR-walk mode for representation of protein sample is proposed. In the proposed model, the sequence order effect is discussed and manifested with a point of the 20-dimension space. And then, the track of protein sample is projected to all of the twenty amino acids, in another word, a protein sample is expressed by a 20-dimension vector. Followed with the preparation work, the proposed mode is applied into four protein datasets. The comparison results indicate that the present method may at least serve as an alternative to the existing predictors in this field.