Signature peptide selection workflow for biomarker quantification using LC-MS-based targeted proteomics

In contrast to quantification of biotherapeutics, endogenous protein biomarker and target quantification using LC-MS based targeted proteomics can require a much more stringent and time-consuming tryptic signature peptide selection for each specific application. While some general criteria exist, there are no tools currently available in the public domain to predict the ionization efficiency for a given signature peptide candidate. Lack of knowledge of the ionization efficiencies forces investigators to choose peptides blindly, thus hindering method development for low abundant protein quantification. Here, the authors propose a tryptic signature peptide selection workflow to achieve a more efficient method development and to improve success rates in signature peptide selection for low abundant endogenous target and protein biomarker quantification.