Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than ؎10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than ؎15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R 2 value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry. Molecular & Cellular Proteomics 5:144 -156, 2006.The study of proteins is crucial in understanding and combating disease through identification of proteins, discovering disease biomarkers, studying protein involvement in specific metabolic pathways, and identifying protein targets in drug discovery (1, 2). An important technique that is used in these studies to quantify and identify peptides and/or proteins present in simple and complex mixtures is ESI-LCMS.To date a majority of the quantitative proteomic analyses have been performed using stable isotope labeling strategies such as ICAT (3), iTRAQ TM (4), SILAC (stable isotope labeling by amino acids in cell culture) (5), and 18 O labeling (6, 7). These methodologies require complex, time-consuming sample preparation and can be relatively expensive.Recently there have been numerous reports applying labelfree methods to monitor the relative abundance of protein between different conditions (8 -11). Relative quantification provides information regarding specific protein abundance changes between two conditions caused by an induced perturbation (environment-induced, drug-induced, and diseaseinduced). These studies require comparison of identical proteolytic peptides in each of the two experiments to accurately determine relative ratios of the particular protein(s) of interest.Rel...

Current methodologies for protein quantitation include 2-dimensional gel electrophoresis techniques, metabolic labeling, and stable isotope labeling methods to name only a few. The current literature illustrates both pros and cons for each of the previously mentioned methodologies. Keeping with the teachings of William of Ockham, "with all things being equal the simplest solution tends to be correct", a simple LC/MS based methodology is presented that allows relative changes in abundance of proteins in highly complex mixtures to be determined. Utilizing a reproducible chromatographic separations system along with the high mass resolution and mass accuracy of an orthogonal time-of-flight mass spectrometer, the quantitative comparison of tens of thousands of ions emanating from identically prepared control and experimental samples can be made. Using this configuration, we can determine the change in relative abundance of a small number of ions between the two conditions solely by accurate mass and retention time. Employing standard operating procedures for both sample preparation and ESI-mass spectrometry, one typically obtains under 5 ppm mass precision and quantitative variations between 10 and 15%. The principal focus of this paper will demonstrate the quantitative aspects of the methodology and continue with a discussion of the associated, complementary qualitative capabilities.

Protein methylation is a common posttranslational modification that mostly occurs on arginine and lysine residues. Arginine methylation has been reported to regulate RNA processing, gene transcription, DNA damage repair, protein translocation, and signal transduction. Lysine methylation is best known to regulate histone function and is involved in epigenetic regulation of gene transcription. To better study protein methylation, we have developed highly specific antibodies against monomethyl arginine; asymmetric dimethyl arginine; and monomethyl, dimethyl, and trimethyl lysine motifs. These antibodies were used to perform immunoaffinity purification of methyl peptides followed by LC-MS/MS analysis to identify and quantify arginine and lysine methylation sites in several model studies. Overall, we identified over 1000 arginine methylation sites in human cell line and mouse tissues, and ∼160 lysine methylation sites in human cell line HCT116. The number of methylation sites identified in this study exceeds those found in the literature to date. Detailed analysis of arginine-methylated proteins observed in mouse brain compared with those found in mouse embryo shows a tissue-specific distribution of arginine methylation, and extends the types of proteins that are known to be arginine methylated to include many new protein types. Many arginine-methylated proteins that we identified from the brain, including receptors, ion channels, transporters, and vesicle proteins, are involved in synaptic transmission, whereas the most abundant methylated proteins identified from mouse embryo are transcriptional regulators and RNA processing proteins.

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC-MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four-protein mixture, the same four-protein mixture spiked into a complex biological background, and a variety of other "system" type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.