Confident Peptide Identifications Research Articles

Utilizing Precursor Ion Connectivity of Different Charge States to Improve Peptide and Protein Identification in MS/MS Analysis.

Tandem mass spectrometry (MS/MS) has become a key method for the structural analysis of biomolecules such as peptides and proteins. A pervasive problem in MS/MS analyses, especially for top-down proteomics, is the occurrence of chimeric spectra, when two or more precursor ions are co-isolated and fragmented, thus leading to complex MS/MS spectra that are populated with fragment ions originating from different precursor ions. This type of convoluted data typically results in low sequence database search scores due to the vast number of mixed-source fragment ions, of which only a fraction originates from a specific precursor ion. Herein, we present a novel workflow that deconvolutes the data of chimeric MS/MS spectra, improving the protein search scores and sequence coverages in database searching and thus providing a more confident peptide and protein identification. Previously misidentified proteins or proteins with insignificant search scores can be correctly and significantly identified following the presented data acquisition and analysis workflow with search scores increasing by a factor of 3-4 for smaller precursor ions (peptides) and >6 for larger precursor ions such as intact ubiquitin and cytochrome C.

Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro.

For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample’s complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide’s monoisotopic mass, which is critical for the peptide’s identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument’s detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

TIDD: tool-independent and data-dependent machine learning for peptide identification

BackgroundIn shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine.ResultsWe propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23–38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience.ConclusionsTIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

MS-Decipher: a user-friendly proteome database search software with an emphasis on deciphering the spectra of O-linked glycopeptides.

The interpretation of mass spectrometry (MS) data is a crucial step in proteomics analysis, and the identification of post-translational modifications (PTMs) is vital for the understanding of the regulation mechanism of the living system. Among various PTMs, glycosylation is one of the most diverse ones. Though many search engines have been developed to decipher proteomic data, some of them are difficult to operate and have poor performance on glycoproteomic datasets compared to advanced glycoproteomic software. To simplify the analysis of proteomic datasets, especially O-glycoproteomic datasets, here, we present a user-friendly proteomic database search platform, MS-Decipher, for the identification of peptides from MS data. Two scoring schemes can be chosen for peptide-spectra matching. It was found that MS-Decipher had the same sensitivity and confidence in peptide identification compared to traditional database searching software. In addition, a special search mode, O-Search, is integrated into MS-Decipher to identify O-glycopeptides for O-glycoproteomic analysis. Compared with Mascot, MetaMorpheus and MSFragger, MS-Decipher can obtain about 139.9%, 48.8% and 6.9% more O-glycopeptide-spectrum matches. A useful tool is provided in MS-Decipher for the visualization of O-glycopeptide-spectra matches. MS-Decipher has a user-friendly graphical user interface, making it easier to operate. Several file formats are available in the searching and validation steps. MS-Decipher is implemented with Java, and can be used cross-platform. MS-Decipher is freely available at https://github.com/DICP-1809/MS-Decipher for academic use. For detailed implementation steps, please see the user guide. Supplementary data are available at Bioinformatics online.

Targeted Mass Spectrometry Analysis of Protein Phosphorylation by Selected Ion Monitoring Coupled to Parallel Reaction Monitoring (tSIM/PRM).

Recent developments in targeted mass spectrometry-based proteomics have provided new methodological solutions for accurate and quantitative analysis of proteins and their posttranslational control, which has significantly advanced our understanding of stress responses in different plant species. Instrumentation allowing high-resolution, accurate-mass (HR/AM) analysis has provided new acquisition strategies for targeted quantitative proteomic analysis by targeted selected ion monitoring (tSIM) and parallel reaction monitoring (PRM). Here we report a sensitive and accurate method for targeted analysis of protein phosphorylation by tSIM coupled to PRM (tSIM/PRM). The tSIM/PRM method takes advantage of HR/AM mass spectrometers and benefits from the combination of highly sensitive precursor ion quantification by tSIM and highly confident peptide identification by spectral library matching in PRM. The detailed protocol describes tSIM/PRM analysis of Arabidopsis thaliana foliar proteins, from the building of a spectral library to sample preparation, mass spectrometry, and data analysis, and provides a methodological approach for specifying the molecular mechanisms of interest.

Escherichia coli and Sf9 Contaminant Databases to Increase Efficiency of Tandem Mass Spectrometry Peptide Identification in Structural Mass Spectrometry Experiments.

Filtering of nonspecifically binding contaminant proteins from affinity purification mass spectrometry (AP-MS) data is a well-established strategy to improve statistical confidence in identified proteins. The CRAPome (contaminant repository for affinity purification) describes the contaminating background content present in many purification strategies. However, full contaminant lists for nickel-nitrilotriacetic acid (NiNTA) and glutathione S-transferase (GST) affinity matrices are lacking. Similarly, no Spodoptera frugiperda (Sf9) contaminants are available, and only the FLAG-purified contaminants are described for Escherichia coli. For MS experiments that use recombinant protein, such as structural mass spectrometry experiments (hydrogen-deuterium exchange mass spectrometry (HDX-MS), chemical cross-linking, and radical foot-printing), failing to include these contaminants in the search database during the initial tandem MS (MS/MS) identification stage can result in complications in peptide identification. We have created contaminant FASTA databases for Sf9 and E. coli NiNTA or GST purification strategies and show that the use of these databases can effectively improve HDX-MS protein coverage, fragment count, and confidence in peptide identification. This approach provides a robust strategy toward the design of contaminant databases for any purification approach that will expand the complexity of systems able to be interrogated by HDX-MS.

Probabilistic Limit of Detection for Ricin Identification Using a Shotgun Proteomics Assay.

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/μL for a 2-μL injection. We also show that, despite the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

ETD-Cleavable Linker for Confident Cross-linked Peptide Identifications.

Peptide cross-links formed using the homobifunctional-linker diethyl suberthioimidate (DEST) are shown to be ETD-cleavable. DEST has a spacer arm consisting of a 6-carbon alkyl chain and it cleaves at the amidino groups created upon reaction with primary amines. In ETD MS2 spectra, DEST cross-links can be recognized based on mass pairs consisting of peptide-NH2• and peptide+linker+NH3 ions, and backbone cleavages are more equally distributed over the two constituent peptides compared with collisional activation. Dead ends that are often challenging to distinguish from cross-links are diagnosed by intense reporter ions. ETD mass pairs can be used in MS3 experiments to confirm cross-link identifications. These features provide a simple but reliable approach to identify cross-links that should facilitate studies of protein complexes.

Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows.

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS3 was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS3 method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS3 approach provides dramatic performance improvements for quantitative multiplexed experiments.

Confidence assignment for mass spectrometry based peptide identifications via the extreme value distribution.

There is a growing trend for biomedical researchers to extract evidence and draw conclusions from mass spectrometry based proteomics experiments, the cornerstone of which is peptide identification. Inaccurate assignments of peptide identification confidence thus may have far-reaching and adverse consequences. Although some peptide identification methods report accurate statistics, they have been limited to certain types of scoring function. The extreme value statistics based method, while more general in the scoring functions it allows, demands accurate parameter estimates and requires, at least in its original design, excessive computational resources. Improving the parameter estimate accuracy and reducing the computational cost for this method has two advantages: it provides another feasible route to accurate significance assessment, and it could provide reliable statistics for scoring functions yet to be developed. We have formulated and implemented an efficient algorithm for calculating the extreme value statistics for peptide identification applicable to various scoring functions, bypassing the need for searching large random databases. The source code, implemented in C ++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit yyu@ncbi.nlm.nih.gov Supplementary data are available at Bioinformatics online.

Preserved Proteins from Extinct Bison latifrons Identified by Tandem Mass Spectrometry; Hydroxylysine Glycosides are a Common Feature of Ancient Collagen

Bone samples from several vertebrates were collected from the Ziegler Reservoir fossil site, in Snowmass Village, Colorado, and processed for proteomics analysis. The specimens come from Pleistocene megafauna Bison latifrons, dating back ∼ 120,000 years. Proteomics analysis using a simplified sample preparation procedure and tandem mass spectrometry (MS/MS) was applied to obtain protein identifications. Several bioinformatics resources were used to obtain peptide identifications based on sequence homology to extant species with annotated genomes. With the exception of soil sample controls, all samples resulted in confident peptide identifications that mapped to type I collagen. In addition, we analyzed a specimen from the extinct B. latifrons that yielded peptide identifications mapping to over 33 bovine proteins. Our analysis resulted in extensive fibrillar collagen sequence coverage, including the identification of posttranslational modifications. Hydroxylysine glucosylgalactosylation, a modification thought to be involved in collagen fiber formation and bone mineralization, was identified for the first time in an ancient protein dataset. Meta-analysis of data from other studies indicates that this modification may be common in well-preserved prehistoric samples. Additional peptide sequences from extracellular matrix (ECM) and non-ECM proteins have also been identified for the first time in ancient tissue samples. These data provide a framework for analyzing ancient protein signatures in well-preserved fossil specimens, while also contributing novel insights into the molecular basis of organic matter preservation. As such, this analysis has unearthed common posttranslational modifications of collagen that may assist in its preservation over time. The data are available via ProteomeXchange with identifier PXD001827.

Conserved Peptide Fragmentation as a Benchmarking Tool for Mass Spectrometers and a Discriminating Feature for Targeted Proteomics

Quantifying the similarity of spectra is an important task in various areas of spectroscopy, for example, to identify a compound by comparing sample spectra to those of reference standards. In mass spectrometry based discovery proteomics, spectral comparisons are used to infer the amino acid sequence of peptides. In targeted proteomics by selected reaction monitoring (SRM) or SWATH MS, predetermined sets of fragment ion signals integrated over chromatographic time are used to identify target peptides in complex samples. In both cases, confidence in peptide identification is directly related to the quality of spectral matches. In this study, we used sets of simulated spectra of well-controlled dissimilarity to benchmark different spectral comparison measures and to develop a robust scoring scheme that quantifies the similarity of fragment ion spectra. We applied the normalized spectral contrast angle score to quantify the similarity of spectra to objectively assess fragment ion variability of tandem mass spectrometric datasets, to evaluate portability of peptide fragment ion spectra for targeted mass spectrometry across different types of mass spectrometers and to discriminate target assays from decoys in targeted proteomics. Altogether, this study validates the use of the normalized spectral contrast angle as a sensitive spectral similarity measure for targeted proteomics, and more generally provides a methodology to assess the performance of spectral comparisons and to support the rational selection of the most appropriate similarity measure. The algorithms used in this study are made publicly available as an open source toolset with a graphical user interface.

Prediction of Peptide Fragment Ion Mass Spectra by Data Mining Techniques

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

Characterization of intact N- and O-linked glycopeptides using higher energy collisional dissociation

Simultaneous elucidation of the glycan structure and the glycosylation site are needed to reveal the biological function of protein glycosylation. In this study, we employed a recent type of fragmentation termed higher energy collisional dissociation (HCD) to examine fragmentation patterns of intact glycopeptides generated from a mixture of standard glycosylated proteins. The normalized collisional energy (NCE) value for HCD was varied from 30 to 60% to evaluate the optimal conditions for the fragmentation of peptide backbones and glycoconjugates. Our results indicated that HCD with lower NCE values preferentially fragmented the sugar chains attached to the peptides to generate a ladder of neutral loss of monosaccharides, thereby enabling the putative glycan structure characterization. In addition, detection of the oxonium ions enabled unambiguous differentiation of glycopeptides from non-glycopeptides. In contrast, HCD with higher NCE values preferentially fragmented the peptide backbone and, thus, provided information needed for confident peptide identification. We evaluated the HCD approach with alternating NCE parameters for confident characterization of intact N- and O-linked glycopeptides in a single liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. In addition, we applied a novel data analysis pipeline, so-called GlycoFinder, to form a basis for automated data analysis. Overall, 38 unique intact glycopeptides corresponding to eight glycosylation sites (six N-linked and two O-linked sites) were confidently identified from a standard protein mixture. This approach provided concurrent characterization of both the peptide and the glycan, thereby enabling comprehensive structural characterization of glycoproteins in a single LC–MS/MS analysis.

Multiplexed and data‐independent tandem mass spectrometry for global proteome profiling

One of the most important early developments in the field of proteomics was the advent of automated data acquisition routines that allowed high-throughput unattended data acquisition during HPLC introduction of peptide mixtures to a tandem mass spectrometer. Prior to this, data acquisition was orders of magnitude less efficient being based entirely on lists of predetermined ions generated in a prior HPLC-MS experiment. This process, known generically as data-dependent analysis, empowered the development of shotgun proteomics where hundreds to thousands of peptide sequences are matched per experiment. In their most popular implementation, the most abundant ionized species from every precursor ion scan at each moment in chromatographic time are successively selected for isolation, activation and tandem mass analysis. While extremely powerful, this strategy has one primary limitation in that detectable dynamic range is restricted (in a top-down manner) to the peptides that ionize the best. To circumvent the serial nature of the data-dependent process and increase detectable dynamic range, the concepts of multiplexed and data-independent acquisition (DIA) have emerged. Multiplexed-data acquisition is based on more efficient co-selection and co-dissociation of multiple precursor ions in parallel, the data from which is subsequently de-convoluted to provide polypeptide sequences for each individual precursor ion. DIA has similar goals, but there is no real-time ion selection based on prior precursor ion scans. Instead, predefined m/z ranges are interrogated either by fragmenting all ions entering the mass spectrometer at every single point in chromatographic time; or by dividing the m/z range into smaller m/z ranges for isolation and fragmentation. These approaches aim to fully utilize the capabilities of mass spectrometers to maximize tandem MS acquisition time and to address the need to expand the detectable dynamic range, lower the limit of detection, and improve the overall confidence of peptide identifications and relative protein quantification measurements. This review covers all aspects of multiplexed- and data-independent tandem mass spectrometry in proteomics, from experimental implementations to advances in software for data interpretation.

HCD-only fragmentation method balances peptide identification and quantitation of TMT-labeled samples in hybrid linear ion trap/orbitrap mass spectrometers

Protein quantitation based on the generation of reporter ions from chemical labels is a widely used quantitative proteomics approach that enables measuring changes in protein abundance in response to biological perturbations. Isobaric labeling strategies at the MS2 level allow simultaneous measurements of different samples but it requires a fine-tuning of the collision energy used in HCD fragmentation to simultaneously obtain confident peptide identifications and highly sensitive and accurate quantitation. Although the recent development of dual CID/HCD fragmentation methods to circumvent these limitations, the fact is that many laboratories still use HCD-only methods for routine TMT protein quantitation experiments. Here, we have explored the effect of the collision energy on peptide identification and quantitation using HCD-only fragmentation methods on a linear ion trap/orbitrap mass spectrometer bearing an axial field HCD fragmentation cell. Our results using the HCD-only method show that a balance between the increase in the number of peptide identifications and the decrease in the precision of peptide quantitation is attained at a normalized collision energy of 40%. The HCD-only method at 40% does not only yield better results than those obtained using a higher collision energies, but it also outperforms the results obtained using the available CID/HCD dual method. In this work we have explored the effect of the collision energy on peptide identification and quantitation using HCD-only fragmentation methods on an Orbitrap Velos Pro mass spectrometer. Our results show that when using a HCD-only method, a balance between the number of peptide identifications and the precision of peptide quantitation is attained at a normalized collision energy (NCE) of 40%. This contrast with the parameters routinely used in many laboratories, which are set at NCE 45%. The single HCD method at 40% does not only yield better results than those obtained using a collision energy of 45% but it also outperforms the results obtained using the available CID/HCD dual method. Therefore, we suggest that the single HCD method using the optimal NCE of 40% can therefore become the method of choice in routinely TMT protein quantitation experiments.

Optimized Nonlinear Gradients for Reversed-Phase Liquid Chromatography in Shotgun Proteomics

Reversed-phase liquid chromatography has become the preferred method for separating peptides in most of the mass spectrometry-based proteomics workflows of today. In the way the technique is typically applied, the peptides are released from the chromatography column by the gradual addition of an organic buffer according to a linear function. However, when applied to complex peptide mixtures, this approach leads to unequal spreads of the peptides over the chromatography time. To address this, we investigated the use of nonlinear gradients, customized for each setup at hand. We developed an algorithm to generate optimized gradient functions for shotgun proteomics experiments and evaluated it for two data sets consisting each of four replicate runs of a human complex sample. Our results show that the optimized gradients produce a more even spread of the peptides over the chromatography run, while leading to increased numbers of confident peptide identifications. In addition, the list of peptides identified using nonlinear gradients differed considerably from those found with the linear ones, suggesting that such gradients can be a valuable tool for increasing the proteome coverage of mass spectrometry-based experiments.

Improving Qualitative and Quantitative Performance for MSE-based Label-free Proteomics

Label-free quantitation by data independent methods (for instance MS(E)) is growing in popularity due to the high technical reproducibility of mass spectrometry analysis. The recent introduction of Synapt hybrid instruments capable of incorporating ion mobility separation within mass spectrometry analysis now allows acquisition of high definition MS(E) data (HDMS(E)). HDMS(E) enables deeper proteome coverage and more confident peptide identifications when compared to MS(E), while the latter offers a higher dynamic range for quantitation. We have developed synapter as, a versatile tool to better evaluate the results of data independent acquisitions on Waters instruments. We demonstrate that synapter can be used to combine HDMS(E) and MS(E) data to achieve deeper proteome coverage delivered by HDMS(E) and more accurate quantitation for high intensity peptides, delivered by MS(E). For users who prefer to run samples exclusively in one mode, synapter allows other useful functionality like false discovery rate estimation, filtering on peptide match type and mass error, and filling missing values. Our software integrates with existing tools, thus permitting us to easily combine peptide quantitation information into protein quantitation by a range of different approaches.

Properties of isotope patterns and their utility for peptide identification in large‐scale proteomic experiments

In proteomic experiments, isotope patterns are routinely generated for all detected peptides. While this pattern is determined by peptide composition, it has not been evaluated as a parameter that can help in the process of peptide identification. First, we investigated how the relative isotope abundance (RIA) accuracy in proteomic data sets depends on the spectral intensity, resolution, and the number of mass spectrometry (MS) 1 scans, using an Orbitrap Velos mass spectrometer. Next, we explored the discriminatory power of isotope patterns in the context of proteome analyses of various complexities, either alone or in combination with a Mascot database search. Finally, we provide a theoretical framework for the required accuracies of both peptide mass and RIA for peptide identification. We demonstrate that the RIA error obtained in routine proteome analyses is 4-5%, and that this is only modestly influenced by spectral intensity, resolution, and the number of MS1 scans. While RIA alone has no discriminatory power, in a Mascot search isotope patterns can distinguish top scoring hits from runner-up hits in 70-95% of cases. Our theoretical approach shows that RIA accuracy needs to be ~0.2% in order to uniquely identify peptides in full proteomes. Our results demonstrate that isotope patterns can have discriminatory power when used in combination with a classical database search. Inclusion of this parameter in proteomic workflows may help to increase confidence in peptide identification, but in practical terms this will be limited to small proteomes.

Data-Dependent Middle-Down Nano-Liquid Chromatography–Electron Capture Dissociation-Tandem Mass Spectrometry: An Application for the Analysis of Unfractionated Histones

Middle-down mass spectrometry (MS) combined with electron capture dissociation (ECD) represents an attractive method for characterization of proteins and their post-translational modifications (PTMs). Coupling online chromatographic separation with tandem mass spectrometry enables a high-throughput analysis, while improving sensitivity of the electrosprayed peptides and reducing sample amount requirements. However, middle-down ECD has not been thus far coupled with online chromatographic separation. In this work, we examine the feasibility of coupling middle-down ECD with online nanoflow-liqiud chromatography (nano-LC) for the analysis of large, >3 kDa, and highly modified polypeptides in a data-dependent acquisition mode. We evaluate the effectiveness of the method by analyzing peptides derived from Asp-N and Glu-C digestions of unfractionated histones from calf thymus and acid-extracted histones from HeLa, MCF-7, and Jurkat cells. Our results demonstrate that middle-down ECD is compatible with online chromatographic separation, providing high peptide and protein sequence coverage while allowing precise mapping of PTM sites. The high mass accuracy, obtained by the ICR mass analyzer, for both the precursor and product ions greatly increases confidence in peptide identification, particularly for modified peptides. Overall, for all samples examined, several histone variants were identified and modification sites were successfully localized, including single, multiple, and positional isomeric PTM sites. The vast majority of the identified peptides were in the mass range from 3 to 9 kDa. The data presented here highlight the feasibility and utility of nano-LC-ECD-MS/MS for high-throughput middle-down analysis.