Use of models of biomacromolecule separation in AMT database generation for shotgun proteomics.

Generation of a complex proteome database requires use of powerful analytical methods capable of following rapid changes in the proteome due to changing physiological and pathological states of the organism under study. One of the promising technologies with this regard is the use of so-called Accurate Mass and Time (AMT) tag peptide databases. Generation of an AMT database for a complex proteome requires combined efforts by many research groups and laboratories, but the chromatography data resulting from these efforts are tied to the particular experimental conditions and, in general, are not transferable from one platform to another. In this work, we consider an approach to solve this problem that is based on the generation of a universal scale for the chromatography data using a multiple-point normalization method. The method follows from the concept of linear correlation between chromatography data obtained over a wide range of separation parameters. The method is further tested for tryptic peptide mixtures with experimental data collected from mutual studies by different independent research groups using different separation protocols and mass spectrometry data processing tools.