[Data filtration for more robust alignment of chromatograms of complex peptide mixtures].

In carrying out proteomic researches using mass-spectrometry there often arises a need to compare experimental data with each other (e.g. control of pathology, the labeled to unlabelled samples). If for peptide identification in different experiments one uses only their exact mass measurements and the retention time in the chromatographic column, difficulties with the identification of chromatographic peaks belonging to the same substances in different chromatograms come up (retention time normalization). Due to inevitable discrepancies in chromatographic conditions of experiments (replacement of chromatographic columns, small changes in mobile phase flow rate or solvent concentration) retention times of the same peptides will diverge from experiment to experiment. In this paper we offer a reliable method for selecting peaks from mass-chromatograms corresponding to the same peptides, which can later be used for retention time normalization (either linear or any other monotone function).

Accurate mass tag retention time database for urine proteome analysis by chromatography--mass spectrometry.

Information about peptides and proteins in urine can be used to search for biomarkers of early stages of various diseases. The main technology currently used for identification of peptides and proteins is tandem mass spectrometry, in which peptides are identified by mass spectra of their fragmentation products. However, the presence of the fragmentation stage decreases sensitivity of analysis and increases its duration. We have developed a method for identification of human urinary proteins and peptides. This method based on the accurate mass and time tag (AMT) method does not use tandem mass spectrometry. The database of AMT tags containing more than 1381 AMT tags of peptides has been constructed. The software for database filling with AMT tags, normalizing the chromatograms, database application for identification of proteins and peptides, and their quantitative estimation has been developed. The new procedures for peptide identification by tandem mass spectra and the AMT tag database are proposed. The paper also lists novel proteins that have been identified in human urine for the first time.