Optimization of peptide selection for absolute protein quantification using QconCATs

Absolute protein quantification is one of the most relevant applications of LC-MS based proteomics. Absolute quantification studies offer the opportunity to unravel complex biological incognitas such as the stoichiometric relation of proteins in subcellular complexes, but also presents higher costs and complexity than relative quantification studies. As a result, development of experimental and bioinformatic solutions that can help simplify absolute protein quantification are of great importance. Among available strategies, the use of heavy-isotope labelled internal standards, such as labelled peptides or full-length proteins, has proven to be one of the most accurate approaches to determine protein concentration and stoichiometric relations. QconCATs (quantification concatamers) are synthetic proteins constituted by a series of concatenated tryptic peptides which can act as heavy-isotope standards for the absolute quantification of multiple proteins in a complex biological sample using a single LC-MS run. Optimal selection of peptides to be included in QconCATs is of utmost importance to enable accurate protein quantification as ideally they are both proteotypic (routinely detected by MS experiments where the parent protein is present) and quantotypic (showing a good correlation between their measured signal intensity and that of the parent protein). Phenomena such as peptide modifications, whether is derived from post translational modifications or sample preparation, or presence of miscleaved species enhanced by conflictive flanking regions can degrade quantification accuracy. Recently, a software tool "AlacatDesigner" was developed to score candidate peptides using algorithms and publicly available data. In this study we selected peptide candidates to be included in QconCATs for the quantification of proteins in translation initiation complexes within whole cell lysates making use of a range of theoretical criteria as well as empiric data that allowed the assessment of candidate peptide quantotypicness. We compared the selected candidates with the top-scoring peptides identified by AlacatDesigner. AlacatDesigner proved to be a useful tool in aiding candidate selection but its top-scoring peptides were not always optimal choices when additional factors were considered such as likelihood of post translational modifications and other empirical evidence. Our analysis demonstrates the importance of using data acquired under equivalent experimental conditions that will be used for quantification experiments when selecting ideal peptide candidates. Information from this study will be used to refine the AlacatDesigner peptide scoring algorithm.