Pupylation sites prediction with ensemble classification model

TLDR: The Neural Network and the Naive Bayesian model have been employed as the classification model and the novel feature is combined appearance of adjacent amino acid and the BLOSUM62 matrix.

Abstract: Post-translational modification of protein is one of the most important biological processions in the field of proteomics and bioinformatics. Pupylation is a novel post translational modification which the small, intrinsically disordered prokaryotic ubiquitin-like protein is conjugated to lysine residues of potential segments. Both the experimental and computational prediction methods of such modified sites have proved to be a challenging issue. Computational methods mainly aimed at extracting effective features from the potential protein segments. In this paper, the statistical feature of adjacent amino acid residues has been proposed and the novel feature is combined appearance of adjacent amino acid and the BLOSUM62 matrix. The Neural Network and the Naive Bayesian model have been employed as the classification model in this work. Such model will also be utilised to deal with many other issues in the field of computational biology.