Functional switches are often regulated by dynamic protein modifications. Assessing protein functions, in vivo, and their functional switches remains still a great challenge in this age of development. An alternative methodology based on in silico procedures may facilitate assessing the multifunctionality of proteins and, in addition, allow predicting functions of those proteins that exhibit their functionality through transitory modifications. Extensive research is ongoing to predict the sequence of protein modification sites and analyze their dynamic nature. This study reports the analysis performed on phosphorylation, Phospho.ELM (version 3.0) and glycosylation, OGlycBase (version 6.0) data for mining association patterns utilizing a newly developed algorithm, MAPRes. This method, MAPRes (Mining Association Patterns among preferred amino acid residues in the vicinity of amino acids targeted for post-translational modifications), is based on mining association among significantly preferred amino acids of neighboring sequence environment and modification sites themselves. Association patterns arrived at by association pattern/rule mining were in significant conformity with the results of different approaches. However, attempts to analyze substrate sequence environment of phosphorylation sites catalyzed for Tyr kinases and the sequence data for O-GlcNAc modification were not successful, due to the limited data available. Using the MAPRes algorithm for developing an association among PTM site with its vicinal amino acids is a valid method with many potential uses: this is indeed the first method ever to apply the association pattern mining technique to protein post-translational modification data.