Background: Through temporally and spatially modified proteins, p0st-translational modifications (PTMs) greatly expand the proteome diversity and play critical roles in regulating the biological processes.Identification of site-specific substrates is fundamental for understanding the molecular mechanisms and biological functions of PTMs, while it is still a great challenge under current technique limitations.To date, the accumulation of experimental discoveries makes it available to develop computational tools for prediction of PTMs.Methods: To predict PTM sites, a previously developed GPS (Group-based Prediction System) algorithm was adopted and improved.Weight training and k-mean clustering methods were introduced for prediction of pupylation sites in prokaryotic proteins and tyrosine nitration sites, respectively.Besides PTMs, GPS algorithm was extended to predict I-Ag7 and HLA-DQ8 epitopes through combination with Gibbs sampling approach.The CPLA database was constructed with manually collected experimental identified lysine acetylation sites from literature.The protein-protein interaction (PPI) information for construction of protein network was collected from five major PPI databases.Results: The GPS algorithm was improved and employed to implement a series of softwares to predict PTMs including GPS-CCD, GPS-PUP and GPS-YNO2 for prediction of calpain cleavage, pupylation, tyrosine nitration site, respectively.Furthermore, the GPS algorithm was extended to develop predictor of GPS-MBA and GPS-ARM for prediction of MHC Class Ⅱ Epitopes and APC/C recognition motif, respectively.With the predictive tools and the pipeline, we systematically compared the functional distribution and preference of S-nitrosylation and nitration.The functional diversity of the D-box and KEN-box mediated APC/C recognition and degradation was also statistically exploited.In addition, by integrating existed protein acetylome data, the human lysine acetylation network (HLAN) was firstly modeled and demonstrated, while the triplet relationship among HAT-substrate-HDAC was proposed as the fundamental component of HLAN.Conclusions: Taken together, since the developed computational tools could provide helpful information with convenience, we anticipated that the combination of computational predictions and experimental verifications will become the foundation of systematically understanding the mechanisms and the dynamics of PTMs .