Ubiquitination is involved in various cellular processes such as protein degradation and stability,
cell cycle progression, transcriptional regulation, antigen processing, DNA repair, inflammation and
regulation of apoptosis, etc. In silico prediction of potential candidate lysine (K) for ubiquitination will not
only save time and money but will also generate valuable data for further scientific research. We developed
Ubipredictor (http://chemdp.com/ubipredictor.php) tool for prediction of potential ubiquitinated lysine in
protein sequences of human, mouse and yeast dataset using LDA. The statistically significant features
selected through LDA were amino acid dimers, position specific score matrix (PSSM) and physicochemical
properties of amino acid like electrostatic charge, heat capacity, codon diversity and secondary structure, etc.
Testing on three different model organism datasets (human, mouse, yeast) showed that the predictive performance of
Ubipredictor was better than two existing tools. On human and mouse datasets, Ubipredictor was found to be more sensitive
than Ubipred and Ubpred. Unlike previously designed tools, we trained Ubipredictor specifically on experimentally verified
ubiquitinated dataset for each of the human mouse and yeast species.