Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The "real world" sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer . The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%). Graphical abstract The analysis workflow employing FractionOptimizer software.
Keywords: Bottom-up proteomics; Liquid chromatography; Mass spectrometry; Peptide fractionation; Proteomics.