A platform for accurate mass and time analyses of mass spectrometry data

We describe an integrated suite of algorithms and software for general accurate mass and time (AMT) tagging data analysis of mass spectrometry data. The AMT approach combines identifications from liquid chromatography (LC) tandem mass spectrometry (MS/MS) data with peptide accurate mass and retention time locations from high-resolution LC-MS data. Our workflow includes the traditional AMT approach, in which MS/MS identifications are located in external databases, as well as methods based on more recent hybrid instruments such as the LTQ-FT or Orbitrap, where MS/MS identifications are embedded with the MS data. We demonstrate our AMT workflow's utility for general data synthesis by combining data from two dissimilar biospecimens. Specifically, we demonstrate its use relevant to serum biomarker discovery by identifying which peptides sequenced by MS/MS analysis of tumor tissue may also be present in the plasma of tumor-bearing and control mice. The analysis workflow, referred to as msInspect/AMT, extends and combines existing open-source platforms for LC-MS/MS (CPAS) and LC-MS (msInspect) data analysis and is available in an unrestricted open-source distribution.     

Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies

Database-searching programs generally identify only a fraction of the spectra acquired in a standard LC/MS/MS study of digested proteins. Subtle variations in database-searching algorithms for assigning peptides to MS/MS spectra have been known to provide different identification results. To leverage this variation, a probabilistic framework is developed for combining the results of multiple search engines. The scores for each search engine are first independently converted into peptide probabilities. These probabilities can then be readily combined across search engines using Bayesian rules and the expectation maximization learning algorithm. A significant gain in the number of peptides identified with high confidence with each additional search engine is demonstrated using several data sets of increasing complexity, from a control protein mixture to a human plasma sample, searched using SEQUEST, Mascot, and X! Tandem database-searching programs. The increased rate of peptide assignments also translates into a substantially larger number of protein identifications in LC/MS/MS studies compared to a typical analysis using a single database-search tool.     

MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments

Background  

Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.     

Optimization of Data-Dependent Acquisition Parameters for Coupling High-Speed Separations with LC-MS/MS for Protein Identifications

Recent developments in chromatography, such as ultra-HPLC and superficially porous particles, offer significantly improved peptide separation. The narrow peak widths, often only several seconds, can permit a 15-min liquid chromatography run to have a similar peak capacity as a 60-min run using traditional HPLC approaches. In theory, these larger peak capacities should provide higher protein coverage and/or more protein identifications when incorporated into a proteomic workflow. We initially observed a decrease in protein coverage when implementing these faster chromatographic approaches, due to data-dependent acquisition (DDA) settings that were not properly set to match the narrow peak widths resulting from newly implemented, fast separation techniques. Oversampling of high-intensity peptides lead to low protein-sequence coverage, and tandem mass spectra (MS/MS) from lower-intensity peptides were of poor quality, as automated MS/MS events were occurring late on chromatographic peaks. These observations led us to optimize DDA settings to use these fast separations. Optimized DDA settings were applied to the analysis of Trypanosome brucei peptides, yielding peptide identifications at a rate almost five times faster than previously used methodologies. The described approach significantly improves protein identification workflows that use typical available instrumentation.     

Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC‐MS phosphopeptide analysis

LC combined with MS/MS analysis of complex mixtures of protein digests is a reliable and sensitive method for characterization of protein phosphorylation. Peptide retention times (RTs) measured during an LC‐MS/MS run depend on both the peptide sequence and the location of modified amino acids. These RTs can be predicted using the LC of biomacromolecules at critical conditions model (BioLCCC). Comparing the observed RTs to those obtained from the BioLCCC model can provide additional validation of MS/MS‐based peptide identifications to reduce the false discovery rate and to improve the reliability of phosphoproteome profiling. In this study, energies of interaction between phosphorylated residues and the surface of RP separation media for both “classic” alkyl C18 and polar‐embedded C18 stationary phases were experimentally determined and included in the BioLCCC model extended for phosphopeptide analysis. The RTs for phosphorylated peptides and their nonphosphorylated analogs were predicted using the extended BioLCCC model and compared with their experimental RTs. The extended model was evaluated using literary data and a complex phosphoproteome data set distributed through the Association of Biomolecular Resource Facilities Proteome Informatics Research Group 2010 study. The reported results demonstrate the capability of the extended BioLCCC model to predict RTs which may lead to improved sensitivity and reliability of LC‐MS/MS‐based phosphoproteome profiling.     

Enhanced identification of peptides lacking basic residues by LC-ESI-MS/MS analysis of singly charged peptides

Peptide sequences lacking basic residues (arginine, lysine, or histidine, referred to as "base-less") are of particular importance in proteomic experiments targeting protein C-termini or employing nontryptic proteases such as GluC or chymotrypsin. We demonstrate enhanced identification of base-less peptides by focused analysis of singly charged precursors in liquid chromatography (LC) electrospray ionization (ESI) tandem mass spectrometry (MS/MS). Singly charged precursors are often excluded from fragmentation and sequence analysis in LC-MS/MS. We generated different pools of base-less and base-containing peptides by tryptic and nontryptic digestion of bacterial proteomes. Focused LC-MS/MS analysis of singly charged precursor ions yielded predominantly base-less peptide identifications. Similar numbers of base-less peptides were identified by LC-MS/M Sanalysis targeting multiply charged precursors. There was little redundancy between the base-less sequences derived by both MS/MS schemes. In the present experimental outcome, additional LC-MS/MS analysis of singly charged precursors substantially increased the identification rate of base-less sequences derived from multiply charged precursors. In conclusion, LC-MS/MS based identification of base-less peptides is substantially enhanced by additional focused analysis of singly charged precursors.     

Clustering millions of tandem mass spectra

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.     

Protein identification assisted by the prediction of retention time in liquid chromatography/tandem mass spectrometry

Two-dimensional liquid chromatography (2D-LC) coupled on-line with electrospray ionization tandem mass spectrometry (2D-LC-ESI-MS/MS) is a new platform for analysis and identification of proteome. Peptides are separated by 2D-LC and then performed MS/MS analysis by tandem MS/MS. The MS/MS data are searched against database for protein identification. In one 2D-LC-ESI-MS/MS run, we obtained not only the structural information of peptides directly from MS/MS, but also the retention time of peptides eluted from LC. Information on the chromatographic behavior of peptides can assist protein identification in the new platform for proteomics. The retention time of the matching peptides of the identified protein was predicted by the hydrophobic contribute of each amino acid on reversed-phase liquid chromatography (RPLC). By using this strategy proteins were identified by four types of information: peptide mass fingerprinting (PMF), sequence query, and MS/MS ions searched and the predicted retention time. This additional information obtained from LC could assist protein identification with no extra experimental cost.     

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.     

Automated identification and quantification of protein phosphorylation sites by LC/MS on a hybrid triple quadrupole linear ion trap mass spectrometer

Complete phosphorylation mapping of protein kinases was successfully undertaken using an automated LC/MS/MS approach. This method uses the direct combination of triple quadrupole and ion trapping capabilities in a hybrid triple quadrupole linear ion trap to selectively identify and sequence phosphorylated peptides. In particular, the use of a precursor ion scan of m/z -79 in negative ion mode followed by an ion trap high resolution scan (an enhanced resolution scan) and a high sensitivity MS/MS scan (enhanced product ion scan) in positive mode is a very effective method for identifying phosphorylation sites in proteins at low femtomole levels. Coupling of this methodology with a stable isotope N-terminal labeling strategy using iTRAQtrade mark reagents enabled phosphorylation mapping and relative protein phosphorylation levels to be determined between the active and inactive forms of the protein kinase MAPKAPK-1 in the same LC/MS run.     

GOAT – A simple LC‐MS/MS gradient optimization tool

Modern nano‐HPLC systems are capable of extremely precise control of solvent gradients, allowing high‐resolution separation of peptides. Most proteomics laboratories use a simple linear analytical gradient for nano‐LC‐MS/MS experiments, though recent evidence indicates that optimized non‐linear gradients result in increased peptide and protein identifications from cell lysates. In concurrent work, we examined non‐linear gradients for the analysis of samples fractionated at the peptide level, where the distribution of peptide retention times often varies by fraction. We hypothesized that greater coverage of these samples could be achieved using per‐fraction optimized gradients. We demonstrate that the optimized gradients improve the distribution of peptides throughout the analysis. Using previous generation MS instrumentation, a considerable gain in peptide and protein identifications can be realized. With current MS platforms that have faster electronics and achieve shorter duty cycle, the improvement in identifications is smaller. Our gradient optimization method has been implemented in a simple graphical tool (GOAT) that is MS‐vendor independent, does not require peptide ID input, and is freely available for non‐commercial use at http://proteomics.swmed.edu/goat/     

Rapid and sensitive identification of major histocompatibility complex class I-associated tumor peptides by Nano-LC MALDI MS/MS

Identification of major histocompatibility complex (MHC)-associated peptides recognized by T-lymphocytes is a crucial prerequisite for the detection and manipulation of specific immune responses in cancer, viral infections, and autoimmune diseases. Unfortunately immunogenic peptides are less abundant species present in highly complex mixtures of MHC-extracted material. Most peptide identification strategies use microcapillary LC coupled to nano-ESI MS/MS in a challenging on-line approach. Alternatively MALDI PSD analysis has been applied for this purpose. We report here on the first off-line combination of nanoscale (nano) LC and MALDI TOF/TOF MS/MS for the identification of naturally processed MHC peptide ligands. These peptides were acid-eluted from human leukocyte antigen (HLA)-A2, HLA-A3, and HLA-B/-C complexes separately isolated from a renal cell carcinoma cell lysate using HLA allele-specific antibodies. After reversed-phase HPLC, peptides were further fractionated via nano-LC. This additional separation step provided a substantial increase in the number of detectable candidate species within the complex peptide pools. MALDI MS/MS analysis on nano-LC-separated material was then sufficiently sensitive to rapidly identify more than 30 novel HLA-presented peptide ligands. Peptide sequences contained perfect anchor amino acid residues described previously for HLA-A2, HLA-A3, and HLA-B7. The most promising candidate for a T-cell epitope is an HLA-B7-binding nonamer peptide derived from the tumor-associated gene NY-BR-16. To demonstrate the sensitivity of our approach we characterized peptides binding to HLA-C molecules that are usually expressed at the cell surface at approximately only 10% the levels of HLA-A or HLA-B. In fact, multiple renal cell carcinoma peptides were identified that contained anchor amino acid residues of HLA-Cw5 and HLA-Cw7. We conclude that the nano-LC MALDI MS/MS approach is a sensitive tool for the rapid and automated identification of MHC-associated tumor peptides.     

Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.     

Direct analysis of protein complexes using mass spectrometry.

We describe a rapid, sensitive process for comprehensively identifying proteins in macromolecular complexes that uses multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) to separate and fragment peptides. The SEQUEST algorithm, relying upon translated genomic sequences, infers amino acid sequences from the fragment ions. The method was applied to the Saccharomyces cerevisiae ribosome leading to the identification of a novel protein component of the yeast and human 40S subunit. By offering the ability to identify >100 proteins in a single run, this process enables components in even the largest macromolecular complexes to be analyzed comprehensively.     

Strategies for the identification of arginine ADP-ribosylation sites

Mono-ADP-ribosylation of arginine is a protein modification in eukaryotic cells regulating protein activity and thereby influencing signal transduction and metabolism. Due to the complexity of the modification and the fragmentation pattern in MS/MS CID experiments, the identification of ADP-ribosylation sites in complex mixtures is difficult. Here we describe a two-step strategy, in the first step enriching and identifying potentially ADP-ribosylated proteins and in the second step identifying the sites of modification by a combination of LC/MS-, LC/MS(E) (MS at elevated fragmentation energy)- and LC/MS/MS experiments. Using this technique we could identify two ADP-ribosylation sites in TNFα digested with trypsin, protease V8 and both proteases and thereby demonstrate the specific ADP-ribosylation of TNFα. In complex samples the detection of ADP-ribosylated peptides requires further enrichment of the modified peptides. We tested various materials routinely used for the isolation of phosphopeptides. IMAC as well as TiO(2) chromatography were successfully applied for the selective enrichment of ADP-ribosylated model peptides.     

Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures

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.     

Protein expression profiling of CLL B cells using replicate off-line strong cation exchange chromatography and LC-MS/MS

In this study we use replicate 2D-LC-MS/MS analyses of crude membranes from B cells derived from a patient with chronic lymphocytic leukemia (CLL) to examine the protein expression profile of CLL B cells. Protein identifications made by replicate 2D-LC-MS/MS analysis of tryptic peptides from detergent solubilized B cell membrane proteins, as well as replicate LC-MS/MS analysis of single off-line strong cation exchange chromatography (SCX) fractions, were analyzed. We show that despite the variance in SCX, capillary LC, and the data-dependent selection of precursor ions, an overlap of 64% between proteins identified in replicate runs was achieved for this system.     

A tool to evaluate correspondence between extraction ion chromatographic peaks and peptide‐spectrum matches in shotgun proteomics experiments

Chromatographed peptide signals form the basis of further data processing that eventually results in functional information derived from data‐dependent bottom‐up proteomics assays. We seek to rank LC/MS parent ions by the quality of their extracted ion chromatograms. Ranked extracted ion chromatograms act as an intuitive physical/chemical preselection filter to improve the quality of MS/MS fragment scans submitted for database search. We identify more than 4900 proteins when considering detector shifts of less than 7 ppm. High quality parent ions for which the database search yields no hits become candidates for subsequent unrestricted analysis for PTMs. Following this rational approach, we prioritize identification of more than 5000 spectrum matches from modified peptides and confirmed the presence of acetylaldehyde‐modified His/Lys. We present a logical workflow that scores data‐dependent selected ion chromatograms and leverage information about semianalytical LC/LC dimension prior to MS. Our method can be successfully used to identify unexpected modifications in peptides with excellent chromatography characteristics, independent of fragmentation pattern and activation methods. We illustrate analysis of ion chromatograms detected in two different modes by RF linear ion trap and electrostatic field orbitrap.     

An enhanced protein crosslink identification strategy using CID-cleavable chemical crosslinkers and LC/MS(n) analysis

We describe a novel two-step LC/MS(n) strategy to effectively and confidently identify numerous crosslinked peptides from complex mixtures. This method incorporates the use of our gas-phase cleavable crosslinking reagent, disuccinimidyl-succinamyl-aspartyl-proline (SuDP), and a new data-processing algorithm CXLinkS (Cleavable Crosslink Selection), which enables unequivocal crosslink peptide selection and identification on the basis of mass measurement accuracy, high resolving power, and the unique fragmentation pattern of each crosslinked peptide. We demonstrate our approach with well-characterized monomeric and multimeric protein systems with and without database searching restrictions where inter-peptide crosslink identification is increased 8-fold over our previously published data-dependent LC/MS3 method and discuss its applicability to other CID-cleavable crosslinkers and more complex protein systems.