Post-translational modifications (PTMs) are under significant focus in molecular biomedicine due to their importance in signal transduction in most cellular and organismal processes. Identification of PTMs, determination of PTM location sites, discrimination between functional and inert PTMs, and quantification of their occupancies are demanding tasks, especially in the light of PTM crosstalk in each biosystem. On top of that, the study of each PTM often necessitates a particular experimental design. Computational approaches can identify the relevant PTMs in a biosystem and help to design follow-up experiments involving specific PTM enrichment. Here, we present a PTM-centric proteome informatic pipeline for prediction of most probable and relevant PTMs in mass spectrometry-based proteomics data and refining raw data search parameters based on the acquired knowledge. Using expression profiling, we identified cellular proteins that are differentially regulated in response to multikinase inhibitors dasatinib and staurosporine at four different concentrations. Computational enrichment analysis was employed to determine the potential PTMs of protein targets for both drugs. Finally, we conducted an additional round of database search with these predicted chemical modifications. Our pipeline helped analyze the enriched PTMs and even detected proteins that were not picked up in the initial search. Our findings support the idea of PTM-oriented searching of MS data in proteomics based on computational enrichment analysis.