We present a novel approach that combines MALDI-TOF profile analysis and bioinformatics-based inclusion criteria to comprehensively predict phosphorylation sites on a single protein of interest from limiting sample. It is technologically difficult to unambiguously identify phosphorylated residues, as many physiologically important phosphorylation sites are of too low abundance in vivo to be unambiguously assigned by mass spectrometry. Conversely, phosphorylation site prediction algorithms, while increasingly accurate, nevertheless overestimate the number of phosphorylation sites. In this study, we show that MODICAS, an MS data management and analysis tool, can be effectively merged with the bioinformatics attributes of residue conservation and phosphosite prediction to generate a short list of putative phosphorylation sites that can be subsequently verified by additional methodologies such as phosphospecific antibodies or mutational analysis. Therefore, the combination of MODICAS driven MS data analysis with bioinformatics-based filtering represents a substantial increase in the ability to putatively identify physiologically relevant phosphosites from limited starting material.
All Science Journal Classification (ASJC) codes
- General Chemistry