Post-translational modifications (PTMs), also called as covalent modifications, are chemical modifying processes of proteins after their translation. By proteolytic cleavage of peptide chains, by adding various functional groups (e.g. phosphorylation) to individual amino acids, or by altering the chemical properties of amino acid residues (e.g. citrullination), various PTMs create or disrupt covalent bonds to change structures, localizations and functions of proteins significantly, play essential roles in almost all of cellular signaling pathways & networks, and determine the cellular dynamics and plasticity. Although more than 350 types of PTMs have been discovered, only a few of them have been well-characterized due to the lack of sufficient data for analyses. Experimental identification of PTM substrates with their sites is labor-intensive and often limited by the availability and optimization of enzymatic reaction. In silico prediction could be a promising strategy to conduct preliminary analyses and greatly reduce the number of potential targets that need further in vivo or in vitro confirmation.
Previously, several types of PTMs have been investigated using computational approaches, e.g. phosphorylation, glycosylation, sulfation and myristoylation, etc. However, the prediction performances of these programs still remain to be improved. The Cuckoo Workgroup focused on developing more rigorous computational models and designing more efficient algorithms to enhance the research of PTMs. Besides the well-know PTM of phosphorylation, we also considered several other new PTMs, including sumoylation, palmitoylation and Lysine/Arginine methylation, etc. We developed several easy-to-use online web tools and downloadable softwares. For example, we constructed GPS and PPSP for prediction of phosphorylation sites, based on GPS and Bayesian Decision Theory algorithms, respectively. And we designed the CSS-Palm to predict the palmitoylation sites. Also, we developed the online tool of MeMo to predict Lysine/Arginine methylation sites, with SVMs algorithm. Moreover, we constructed an online tool of SUMOsp to predict sumoylation sites, mainly with the GPS algorithm. We also surveyed the the functional diversity of SUMO substrates, and carried out large-scale prediction of Protein Kinase A (PKA) specific substrates. More analyses will be available in the near future.
Our aim is to develop novel algorithms and computational softwares for understanding the temporally and spatially regulatory roles of post-translational modifications involved in cellular signaling pathways and networks. We believe our and others computational studies together with experimental identifications will propel the research of PTMs into a new phase.
