Computational Prediction of Lysine Pupylation Sites in Prokaryotic Proteins Using Position Specific Scoring Matrix into Bigram for Feature Extraction

Abstract

Post-transcriptional modification (PTM) in a form of covalently attached proteins like ubiquitin (Ub) are considered an exclusive feature of eukaryotic organisms. Pupylation, a crucial type of PTM of prokaryotic proteins, is modification of lysine residues with a prokaryotic ubiquitin-like protein (Pup) tagging functionally to ubiquitination used by certain bacteria in order to target proteins for proteasomal degradation. Pupylation plays an important role in regulating many biological processes and accurate identification of pupylation sites contributes in understanding the molecular mechanism of pupylation. The experimental technique used in identification of pupylated lysine residues is still a costly and time-consuming process. Thus, several computational predictors have been developed based on protein sequence information to tackle this crucial issue. However, the performance of these predictors are still unsatisfactory. In this work, we propose a new predictor, PSSM-PUP that uses evolutionary information of amino acids to predict pupylated lysine residues. Each lysine residue is defined through its profile bigrams extracted from position specific scoring matrices (PSSM). PSSM-PUP has demonstrated improvement in performance compared to other existing predictors using the benchmark dataset from Pupdb Database. The proposed method achieves highest performance in 10-fold PSSM-PUP with accuracy value of 0.8975, sensitivity value of 0.8731, specificity value of 0.9222, precision value of 0.9222 and Matthews correlation coefficient value of 0.801.