The identification of proteins separated by two-dimensional (2D) gel electrophoresis is crucial to proteome projects aiming at characterization of all proteins expressed by a given organism or tissue in a given circumstance. The use of 2D gel electrophoresis in functional genomic research has proved to be an essential tool to understand the differential expression of protein isoforms. Structural alterations of proteins can reflect their conformational and functional changes and, consequently, their role in tissue physiology. Therefore, there is a massive accumulation of bioinformatics resources that could be used to obtain presumptive information about posttranslational modifications (PTM), including the reversible phosphorylation of proteins. Manual characterization of phosphorylation sites in experimental data is a long, difficult and laborious work to proteomics researchers. However, new and numerous bioinformatics tools for phosphorylation prediction are now located in the web. Using 2D electrophoresis experimental and analytical data (isoelectric point and molecular weight, as well as information retrieved after proteomic databases searches), we have performed in silico identification of phosphorylated sites that can be useful for PTM study. Comparing the profile of the "train of phosphorylated proteins", comprised by twelve isoforms of the gastrocnemic muscle protein model, we have found a qualitative correlation between the in silico prediction and the experimental data. Comparison of putative phosphorylation sites of proteins from three species (rat, mouse and human) and our protein model, revealed the high conservation of motifs responsible for phosphate ligation. The phosphoserine site was found to be the most abundant in gastrocnemic muscle proteins. Particularly, the differential expression of a specific isoform is discussed. Proteins from the gastrocnemic muscle have presented a relative molecular weight (rMW) compatible with the proteins used as comparative model. Thus, the current comparison is useful to predict probable PTMs and to give support to more detailed studies about protein modification.