IMAC/TiO(2) enrich for peptide modifications other than phosphorylation: implications for chromatographic choice and database searching in phosphoproteomics

Protein regulation by reversible phosphorylation is fundamental in nature, and large-scale phosphoproteomic analyses are becoming routine in proteomics laboratories. These analyses utilise phosphopeptide separation and enrichment techniques linked to LC-MS/MS. Herein, we report that IMAC and TiO(2) also enrich for non-phosphorylated modified peptides such as acetylated, deamidated and carbamylated peptides. Urea and digestion conditions commonly used in phosphoproteomic workflows are the likely sources of the induced modifications (deamidation and carbamylation) and can easily modify phosphopeptides. Including these variable modifications in database searches increased the total number of identified phosphopeptides by 15%. We also show that strong cation exchange fractionation provides poor resolution of phosphopeptides and actually enriches these alternatively modified peptides. By switching to reverse-phase chromatography, we show a significant improvement in the number of identified phosphopeptides. We recommend that the users of phosphopeptide enrichment strategies avoid using urea as a denaturant and that careful consideration is given to chromatographic conditions and the types of variable modifications used in database searches. Thus, the capacity of IMAC and TiO(2) to enrich phosphopeptides bearing modifications other than phosphorylation is a previously unappreciated property of these chromatographies with practical implications for the field of phosphoproteomics.     

Reference-facilitated phosphoproteomics: fast and reliable phosphopeptide validation by microLC-ESI-Q-TOF MS/MS

Recent advances in instrument control and enrichment procedures have enabled us to quantify large numbers of phosphoproteins and record site-specific phosphorylation events. An intriguing problem that has arisen with these advances is to accurately validate where phosphorylation events occur, if possible, in an automated manner. The problem is difficult because MS/MS spectra of phosphopeptides are generally more complicated than those of unmodified peptides. For large scale studies, the problem is even more evident because phosphorylation sites are based on single peptide identifications in contrast to protein identifications where at least two peptides from the same protein are required for identification. To address this problem we have developed an integrated strategy that increases the reliability and ease for phosphopeptide validation. We have developed an off-line titanium dioxide (TiO(2)) selective phosphopeptide enrichment procedure for crude cell lysates. Following enrichment, half of the phosphopeptide fractionated sample is enzymatically dephosphorylated, after which both samples are subjected to LC-MS/MS. From the resulting MS/MS analyses, the dephosphorylated peptide is used as a reference spectrum against the original phosphopeptide spectrum, in effect generating two peptide spectra for the same amino acid sequence, thereby enhancing the probability of a correct identification. The integrated procedure is summarized as follows: 1) enrichment for phosphopeptides by TiO(2) chromatography, 2) dephosphorylation of half the sample, 3) LC-MS/MS-based analysis of phosphopeptides and corresponding dephosphorylated peptides, 4) comparison of peptide elution profiles before and after dephosphorylation to confirm phosphorylation, and 5) comparison of MS/MS spectra before and after dephosphorylation to validate the phosphopeptide and its phosphorylation site. This phosphopeptide identification represents a major improvement as compared with identifications based only on single MS/MS spectra and probability-based database searches. We investigated an applicability of this method to crude cell lysates and demonstrate its application on the large scale analysis of phosphorylation sites in differentiating mouse myoblast cells.     

Improved titanium dioxide enrichment of phosphopeptides from HeLa cells and high confident phosphopeptide identification by cross-validation of MS/MS and MS/MS/MS spectra

Enrichment is essential for phosphoproteome analysis because phosphorylated proteins are usually present in cells in low abundance. Recently, titanium dioxide (TiO2) has been demonstrated to enrich phosphopeptides from simple peptide mixtures with high specificity; however, the technology has not been optimized. In the present study, significant non-specific bindings were observed when proteome samples were applied to TiO2 columns. Column wash with an NH4Glu solution after loading peptide mixtures significantly increased the efficiency of TiO2 phosphopeptide enrichment with a recovery of up to 84%. Also, for proteome samples, more than a 2-fold increase in unique phosphopeptide identifications has been achieved. The use of NH4Glu for a TiO2 column wash does not significantly reduce the phosphopeptide recovery. A total of 858 phosphopeptides corresponding to 1034 distinct phosphosites has been identified from HeLa cells using the improved TiO2 enrichment procedure in combination with data-dependent neutral loss nano-RPLC-MS2-MS3 analysis. While 41 and 35% of the phosphopeptides were identified only by MS2 and MS3, respectively, 24% was identified by both MS2 and MS3. Cross-validation of the phosphopeptide assignment by MS2 and MS3 scans resulted in the highest confidence in identification (99.5%). Many phosphosites identified in this study appear to be novel, including sites from antigen Ki-67, nucleolar phosphoprotein p130, and Treacle protein. The study also indicates that evaluation of confidence levels for phosphopeptide identification via the reversed sequence database searching strategy might underestimate the false positive rate.     

Global profiling of phosphopeptides by titania affinity enrichment

Protein phosphorylation is a ubiquitous post-translational modification critical to many cellular processes. Large-scale unbiased characterization of phosphorylation status remains a major technical challenge in proteomics. In the present work, we evaluate and optimize titania-based affinity enrichment for global profiling of phosphopeptides from complex biological mixtures. We demonstrate that inclusion of glutamic acid in the sample loading buffer substantially reduced nonspecific binding of nonphosphorylated peptides to the titania while retaining the high binding affinity for phosphopeptides. The reduction in nonspecific peptide binding enhanced overall phosphopeptide recovery, ranging from 22 to 85%, and led to substantial improvement in large-scale global profiling. In addition, we observed that the overall identification of phosphopeptides was significantly enhanced by neutral loss-triggered MS (3) scans and respective use of multiple charge- and mass-dependent filtering criteria for MS (2) and MS (3) spectra. In conjunction with strong-cation exchange chromatography (SCX) for prefractionation, a total of 4002 distinct phosphopeptides were identified from SKBr3 breast cancer cells at false-positive rates of 3.7% and 5.5%, respectively, for singly and doubly phosphorylated peptides.     

Identification of phosphopeptides with unknown cleavage specificity by a de novo sequencing assisted database search strategy

In theory, proteases with broad cleavage specificity could be applied to digest protein samples to improve the phosphoproteomic analysis coverage. However, in practice this approach is seldom employed. This is because the identification of phosphopeptides without enzyme specificity by conventional database search strategy is extremely difficult due to the huge search space. In this study, we investigated the performance of a de novo sequencing assisted database search strategy for the identification of such phosphopeptides. Firstly, we compared the performance of conventional database search strategy and the de novo sequencing assisted database search strategy for the identification of peptides and phosphopeptides without stetting enzyme specificity. It was found that the identification sensitivity dropped significantly for the conventional one while it was only slightly decreased for the new approach. Then, this new search strategy was applied to identify phosphopeptides generated by Proteinase K digestion, which resulted in the identification of 717 phosphopeptides. Finally, this strategy was utilized for the identification of serum endogenous phosphopeptides, which were generated in vivo by different kinds of proteases and kinases, and the identification of 68 unique serum endogenous phosphopepitdes was successfully achieved.     

Mitochondrial phosphoproteome revealed by an improved IMAC method and MS/MS/MS

IMAC in combination with mass spectrometry is a promising approach for global analysis of protein phosphorylation. Nevertheless this approach suffers from two shortcomings: inadequate efficiency of IMAC and poor fragmentation of phosphopeptides in the mass spectrometer. Here we report optimization of the IMAC procedure using (32)P-labeled tryptic peptides and development of MS/MS/MS (MS3) for identifying phosphopeptide sequences and phosphorylation sites. The improved IMAC method allowed recovery of phosphorylated tryptic peptides up to approximately 77% with only minor retention of unphosphorylated peptides. MS3 led to efficient fragmentation of the peptide backbone in phosphopeptides for sequence assignment. Proteomics of mitochondrial phosphoproteins using the resulting IMAC protocol and MS3 revealed 84 phosphorylation sites in 62 proteins, most of which have not been reported before. These results revealed diverse phosphorylation pathways involved in the regulation of mitochondrial functions. Integration of the optimized batchwise IMAC protocol with MS3 offers a relatively simple and more efficient approach for proteomics of protein phosphorylation.     

Catch me if you can: mass spectrometry-based phosphoproteomics and quantification strategies

Phosphorylation of proteins is one of the most prominent PTMs and for instance a key regulator of signal transduction. In order to improve our understanding of cellular phosphorylation events, considerable effort has been devoted to improving the analysis of phosphorylation by MS-based proteomics. Different enrichment strategies for phosphorylated peptides/proteins, such as immunoaffinity chromatography (IMAC) or titanium dioxide, have been established and constantly optimized for subsequent MS analysis. Concurrently, specific MS techniques were developed for more confident identification and phosphorylation site localization. In addition, more attention is paid to the LC-MS instrumentation to avoid premature loss of phosphorylated peptides within the analytical system. Despite major advances in all of these fields, the analysis of phosphopeptides still remains far from being routine in proteomics. However, to reveal cellular regulation by phosphorylation events, not only qualitative information about the phosphorylation status of proteins but also, in particular, quantitative information about distinct changes in phosphorylation patterns upon specific stimulation is mandatory. Thus, yielded insights are of outstanding importance for the emerging field of systems biology. In this review, we will give an insight into the historical development of phosphoproteome analysis and discuss its recent progress particularly regarding phosphopeptide quantification and assessment of phosphorylation stoichiometry.     

Automatic validation of phosphopeptide identifications by the MS2/MS3 target-decoy search strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.     

Integrating titania enrichment,iTRAQ labeling,and Orbitrap CID‐HCD for global identification and quantitative analysis of phosphopeptides

Recent advances in MS instrumentation and progresses in phosphopeptide enrichment, in conjunction with more powerful data analysis tools, have facilitated unbiased characterization of thousands of site‐specific phosphorylation events. Combined with stable isotope labeling by amino acids in cell culture metabolic labeling, these techniques have made it possible to quantitatively evaluate phosphorylation changes in various physiological states in stable cell lines. However, quantitative phosphoproteomics in primary cells and tissues remains a major technical challenge due to the lack of adequate techniques for accurate quantification. Here, we describe an integrated strategy allowing for large scale quantitative profiling of phosphopeptides in complex biological mixtures. In this technique, the mixture of proteolytic peptides was subjected to phosphopeptide enrichment using a titania affinity column, and the purified phosphopeptides were subsequently labeled with iTRAQ reagents. After further fractionation by strong‐cation exchange, the peptides were analyzed by LC‐MS/MS on an Orbitrap mass spectrometer, which collects CID and high‐energy collisional dissociation (HCD) spectra sequentially for peptide identification and quantitation. We demonstrate that direct phosphopeptide enrichment of protein digests by titania affinity chromatography substantially improves the efficiency and reproducibility of phosphopeptide proteomic analysis and is compatible with downstream iTRAQ labeling. Conditions were optimized for HCD normalized collision energy to balance the overall peptide identification and quantitation using the relative abundances of iTRAQ reporter ions. Using this approach, we were able to identify 3557 distinct phosphopeptides from HeLa cell lysates, of which 2709 were also quantified from HCD scans.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

SP19A Phosphoprotein profiling by negative mode precursor ion scanning

An important goal in cancer research is to monitor phosphoprotein changes in order to identify downstream targets of dysregulated signaling pathways. We used a precursor ion scanning approach described by Carr et al,¹ which identifies phosphopeptides in negative ion mode by their loss of a −79-Da signature ion (PO_3⁻). We first compared three methods for phosphopeptide detection in the protein kinase, Mps1. Using a 4000 QTrap mass spectrometer, standard analysis by LC/MS/MS in positive mode identified 27 phosphopeptides containing 22 phosphosites. Precursor ion scanning in negative mode using the same instrument identified 47 phosphopeptides containing 34 phosphosites, with detection sensitivity ~10 fmol. Using a LTQ-Orbitrap mass spectrometer, MS³ on peptide ions that underwent neutral loss of H₃PO₄ during MS/MS identified 30 phosphopeptides and 28 phosphosites. Thus, precursor ion scanning showed the highest performance in identifying phosphopeptides in simple mixtures.Next, we examined human melanoma cells treated with and without U0126, a drug that inhibits the constitutively activated B-Raf/MAPK pathway. Cytosolic proteins were resolved by SAX-FPLC, and proteins in each fraction were proteolyzed. Peptides in each fraction were separated by nanoflow RP-HPLC and phosphopeptides monitored by precursor ion scanning, triggering MS/MS upon detection of the −79-Da signature ion. In parallel, peptides were analyzed by positive mode LC/MS/MS in order to monitor protein abundance changes by spectral counting. In-house algorithms utilizing OpenMS modules were developed to detect phosphopeptide peaks, match them to MS/MS spectra, group peaks over consecutive fractions, and quantify and sum intensities. More than 20,000 peaks could be detected over all SAX fractions, representing ~5000 grouped phosphopeptide candidates. About 350 phosphopeptides were manually validated, of which ~10% were responsive to drug treatment. Thus, targets of dysregulated B-Raf/MAPK signaling in melanoma can be identified using precursor ion scanning and detection of phosphopeptides in complex samples.     

When less can yield more – Computational preprocessing of MS/MS spectra for peptide identification

The effectiveness of database search algorithms, such as Mascot, Sequest and ProteinPilot is limited by the quality of the input spectra: spurious peaks in MS/MS spectra can jeopardize the correct identification of peptides or reduce their score significantly. Consequently, an efficient preprocessing of MS/MS spectra can increase the sensitivity of peptide identification at reduced file sizes and run time without compromising its specificity. We investigate the performance of 25 MS/MS preprocessing methods on various data sets and make software for improved preprocessing of mgf/dta‐files freely available from http://hci.iwr.uni‐heidelberg.de/mip/proteomics or http://www.childrenshospital.org/research/steenlab .     

Colander: a probability-based support vector machine algorithm for automatic screening for CID spectra of phosphopeptides prior to database search

We developed a probability-based machine-learning program, Colander, to identify tandem mass spectra that are highly likely to represent phosphopeptides prior to database search. We identified statistically significant diagnostic features of phosphopeptide tandem mass spectra based on ion trap CID MS/MS experiments. Statistics for the features are calculated from 376 validated phosphopeptide spectra and 376 nonphosphopeptide spectra. A probability-based support vector machine (SVM) program, Colander, was then trained on five selected features. Data sets were assembled both from LC/LC-MS/MS analyses of large-scale phosphopeptide enrichments from proteolyzed cells, tissues and synthetic phosphopeptides. These data sets were used to evaluate the capability of Colander to select pS/pT-containing phosphopeptide tandem mass spectra. When applied to unknown tandem mass spectra, Colander can routinely remove 80% of tandem mass spectra while retaining 95% of phosphopeptide tandem mass spectra. The program significantly reduced computational time spent on database search by 60-90%. Furthermore, prefiltering tandem mass spectra representing phosphopeptides can increase the number of phosphopeptide identifications under a predefined false positive rate.     

Enhancement of the efficiency of phosphoproteomic identification by removing phosphates after phosphopeptide enrichment

Immobilized metal affinity chromatography (IMAC) and titanium oxide (TiO2) chromatography are simple, widely used, and cost-effective methods to enrich phosphopeptides, but the sample loading buffer composition, desalting procedure, and control of loading amount are critical to avoid nonspecific interactions and to achieve efficient phosphopeptide enrichment. Although the combination of MS3 analysis and high-resolution mass spectrometry (MS) is helpful to identify phosphopeptides, the quality of many MS/MS spectra having a neutral loss peak of phosphate is still too poor to allow sequence identification, and this results in many false-negative as well as false-positive identifications. Here, we present a novel strategy, which is based on the use of alkaline phosphatase to remove phosphates and analysis of phospho/dephosphopeptide retention times to increase the reliability of identification. The use of phospho/dephosphopeptide retention time ratios allows the identification of phosphopeptides with high confidence with the aid of a focused database of dephosphopeptides. This approach was very effective to identify multiple phophorylations in tryptic peptides. A 'true' phosphorylation data set should contain about 90% phospho-Ser and a few percent phospho-Tyr, and this ratio can be used as a quality criterion for evaluation of data sets. By applying this efficient approach, we were able to identify more than one thousand phosphopeptides.     

Comparison of ERLIC-TiO2, HILIC-TiO2, and SCX-TiO2 for global phosphoproteomics approaches

Reversible phosphorylations play a critical role in most biological pathways. Hence, in signaling studies great effort has been put into identification of a maximum number of phosphosites per experiment. Mass spectrometry (MS)-based phosphoproteomics approaches have been proven to be an ideal analytical method for mapping of phosphosites. However, because of sample complexity, fractionation of phosphopeptides prior to MS analysis is a crucial step. In the current study, we compare the chromatographic strategies electrostatic repulsion-hydrophilic interaction chromatography (ERLIC), hydrophilic interaction liquid chromatography (HILIC), and strong cation exchange chromatography (SCX) for their fractionation behavior of phosphopeptides. In addition, we investigate the use of repetitive TiO(2)-based enrichment steps for a maximum identification of phosphopeptides. On the basis of our results, SCX yields the highest number of identified phosphopeptides, whereas ERLIC is optimal for the identification of multiphosphorylated peptides. Consecutive incubations of fractions and flow-through by TiO(2) beads enrich qualitatively different sets of phosphopeptides, increasing the number of identified phosphopeptides per analysis.     

Integration of conventional quantitative and phospho‐proteomics reveals new elements in activated Jurkat T‐cell receptor pathway maintenance

Recent years have seen a constant development of tools for the global assessment of phosphoproteins. Here, we outline a concept for integrating approaches for quantitative proteomics and phosphoproteomics. The strategy was applied to the analysis of changes in signalling and protein synthesis occurring after activation of the T‐cell receptor (TCR) pathway in a T‐cell line (Jurkat cells). For this purpose, peptides were obtained from four biological replicates of activated and control Jurkat T‐cells and phosphopeptides enriched via a TiO₂‐based chromatographic step. Both phosphopeptide‐enriched and flow‐through fractions were analyzed by LC–MS. We observed 1314 phosphopeptides in the enriched fraction whereas 19 were detected in the flow‐through, enabling the quantification of 414 and eight phosphoproteins in the respective fractions. Pathway analysis revealed the differential regulation of many metabolic pathways. Among the quantified proteins, 11 kinases with known TCR‐related function were detected. A kinase‐substrate database search for the phosphosites identified also confirmed the activity of a further ten kinases. In total, these two approaches provided evidence of 19 unique TCR‐related kinases. The combination of phosphoproteomics and conventional quantitative shotgun analysis leads to a more comprehensive assessment of the signalling networks needed for the maintenance of the activated status of Jurkat T‐cells.     

Occurrence and detection of phosphopeptide isomers in large-scale phosphoproteomics experiments

The past decade has been marked by the emergence of selective affinity media and sensitive mass spectrometry instrumentation that facilitated large-scale phosphoproteome analyses and expanded the repertoire of protein phosphorylation. Despite these remarkable advances, the precise location of the phosphorylation site still represents a sizable challenge in view of the labile nature of the phosphoester bond and the presence of neighboring phosphorylatable residues within the same peptide. This difficulty is exacerbated by the combinatorial distribution of phosphorylated residues giving rise to different phosphopeptide isomers. These peptides have similar physicochemical properties, and their separation by LC is often problematic. Few studies have described the frequency and distribution of phosphoisomers in large-scale phosphoproteomics experiments, and no convenient informatics tools currently exist to facilitate their detection. To address this analytical challenge, we developed two algorithms to detect separated and co-eluting phosphopeptide isomers and target their subsequent identification using an inclusion list in LC-MS/MS experiments. Using these algorithms, we determined that the proportion of isomers present in phosphoproteomics studies from mouse, rat, and fly cell extracts represents 3-6% of all identified phosphopeptides. While conventional analysis can identify chromatographically separated phosphopeptides, targeted LC-MS/MS analyses using inclusion lists provided complementary identification and expanded the number of phosphopeptide isomers by at least 52%. Interestingly, these analyses revealed that the occurrence of phosphopeptides isomers can also correlate with the presence of extended phosphorylatable amino acids that can act as a "phosphorylation switch" to bind complementary domains such as those present in SR proteins and ribonucleoprotein complexes.     

Mining phosphopeptide signals in liquid chromatography-mass spectrometry data for protein phosphorylation analysis

Protein phosphorylation is a key post-translational modification that governs biological processes. Despite the fact that a number of analytical strategies have been exploited for the characterization of protein phosphorylation, the identification of protein phosphorylation sites is still challenging. We proposed here an alternative approach to mine phosphopeptide signals generated from a mixture of proteins when liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis is involved. The approach combined dephosphorylation reaction, accurate mass measurements from a quadrupole/time-of-flight mass spectrometer, and a computing algorithm to differentiate possible phosphopeptide signals obtained from the LC-MS analyses by taking advantage of the mass shift generated by alkaline phosphatase treatment. The retention times and m/z values of these selected LC-MS signals were used to facilitate subsequent LC-MS/MS experiments for phosphorylation site determination. Unlike commonly used neutral loss scan experiments for phosphopeptide detection, this strategy may not bias against tyrosine-phosphorylated peptides. We have demonstrated the applicability of this strategy to sequence more, in comparison with conventional data-dependent LC-MS/MS experiments, phosphopeptides in a mixture of alpha- and beta-caseins. The analytical scheme was applied to characterize the nasopharyngeal carcinoma (NPC) cellular phosphoproteome and yielded 221 distinct phosphorylation sites. Our data presented in this paper demonstrated the merits of computation in mining phosphopeptide signals from a complex mass spectrometric data set.     

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.