Accurately predicting microbial phosphorylation sites using evolutionary and structural features

Abstract

Post-translational modification (PTM) is a biological process involving a protein's enzymatic changes after its translation by the ribosome. Phosphorylation is one of the most critical PTMs that occurs when a phosphate group interacts with an amino acid residue along protein sequence. It contributes to cell communication, DNA repair, and gene regulation. Predicting microbial phosphorylation sites can provide better understanding of host-pathogen interaction and the development of anti-microbial agents. Experimental methods such as mass spectrometry are time-consuming, laborious, and expensive. This paper proposes a new approach, called RotPhoPred, for predicting phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites in the microbial organism by integrating evolutionary bigram profile with structural information and using Rotation Forest as the classification technique. To the best of our knowledge, our extracted features and employed classifier have never been utilized for this task. Comparative results demonstrate that the RotPhoPred surpasses its peers in terms of different metrics such as sensitivity (90.0%, 75.4% and 78.2%), specificity (92.1%, 97.2% and 94.7%), accuracy (91.0%, 86.3%, 86.4%), and MCC (0.82, 0.74 and 0.74) for pS, pT, and pY sites predictions, respectively. RotPhoPred as a standalone predictor and all its source codes are publicly available at: https://github.com/faisalahm3d/RotPredPho.

Keywords: Classification; Evolutionary features; Machine learning; Phosphorylation; Post translational modification; Structural features.