The effect of three novel feature extraction methods on the prediction of the subcellular localization of multi-site virus proteins

Experimental methods play a crucial role in identifying the subcellular localization of proteins and building high-quality databases. However, more efficient, automated computational methods are required to predict the subcellular localization of proteins on a large scale. Various efficient feature extraction methods have been proposed to predict subcellular localization, but challenges remain. In this paper, three novel feature extraction methods are established to improve multi-site prediction. The first novel feature extraction method utilizes repetitive information via moving windows based on a dipeptide pseudo amino acid composition method (R-Dipeptide). The second novel feature extraction method utilizes the impact of each amino acid residue on its following residues based on pseudo amino acids (I-PseAAC). The third novel feature extraction method provides local information about protein sequences that reflects the strength of the physicochemical properties of residues (PseAAC2). The multi-label k-nearest neighbor algorithm (MLKNN) is used to predict the subcellular localization of multi-site virus proteins. The best overall accuracy values of R-Dipeptide, I-PseAAC, and PseAAC2 when applied to dataset S from Virus-mPloc are 59.92%, 59.13%, and 57.94% respectively.