Bioinformatics Of Protein Post-translational Modifications And Cell Signaling Pathways

In the Ph.D. thesis, I give a brief introduction of my scientific progress on bioinformatics during the past five years. My major interest is bioinformatics of post-translational modifications (PTMs) of protein and cell signaling pathways. By covalently attaching to individual amino acids various functional molecules such as phosphates, lipids, or proteins, post-translational modifications alter a protein's biochemical nature significantly, and play key roles in a wide variety of cellular processes. 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 proteins' PTM 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. We focused on developing more rigorous computational models and employing 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. For example, we construct GPS and PPSP for phosphorylation site prediction, based on GPS and Bayesian Decision Theory algorithms, respectively. And we deployed the CSS approach to predict the palmitoylation site. Also, we developed the online tool of MeMo to prediction Lysine/Arginine methylation site, with SVMs algorithm. Moreover, we construct an online tool of SUMOsp to predict sumoylation site, with GPS and Motifx approaches. We also surveyed the the functional diversity of SUMO substrates. And more analyses will be available in the future.