A FEATURE-INTEGRATED MACHINE LEARNING FRAMEWORK FOR LYSINE PTM SITE CLASSIFICATION

Post-translational modification (PTM) increases the functional diversity of proteins by
introducing new functional groups to the side chain of amino acid of a protein. The structural and
functional diversities of proteins as well as plasticity and dynamics of living cells are significantly
dominated by the post-translational modifications (PTMs). It plays an important role in diversity,
structure, plasticity, active cells even in human diseases and so on. PTMs are also responsible for
expanding the genetic code and for regulating cellular physiology. Like other PTMs, PTM of
lysine residues have proven to be major regulators of gene expression, protein-protein interactions,
and protein processing and degradation. So far so many computational methods have been
developed to identify PTM of lysine but most of them are binary classifier. It can predict various
single-label PTM sites, and a very few have been developed to solve multi-label PTM of lysine.
Our expectation is to design a simple and efficient predictor for predicting multiple lysine PTM
sites. Our goal is to get higher success rates in comparison with the existing predictors in this area.