Protein identification by nanopore peptide profiling.

Nanopores are single-molecule sensors used in nucleic acid analysis, whereas their applicability towards full protein identification has yet to be demonstrated. Here, we show that an engineered Fragaceatoxin C nanopore is capable of identifying individual proteins by measuring peptide spectra that are produced from hydrolyzed proteins. Using model proteins, we show that the spectra resulting from nanopore experiments and mass spectrometry share similar profiles, hence allowing protein fingerprinting. The intensity of individual peaks provides information on the concentration of individual peptides, indicating that this approach is quantitative. Our work shows the potential of a low-cost, portable nanopore-based analyzer for protein identification.

The NISTmAb Reference Material 8671 value assignment, homogeneity, and stability.

The NISTmAb Reference Material (RM) 8671 is intended to be an industry standard monoclonal antibody for pre-competitive harmonization of best practices and designing next generation characterization technologies for identity, quality, and stability testing. It must therefore embody the quality and characteristics of a typical biopharmaceutical product and be available long-term in a stable format with consistent product quality attributes. A stratified sampling and analysis plan using a series of qualified analytical and biophysical methods is described that assures RM 8671 meets these criteria. Results for the first three lots of RM 8671 highlight the consistency of material attributes with respect to size, charge, and identity. RM 8671 was verified to be homogeneous both within and between vialing lots, demonstrating the robustness of the lifecycle management plan. It was analyzed in concert with the in-house primary sample 8670 (PS 8670) to provide a historical link to this seminal material. RM 8671 was verified to be fit for its intended purpose as a technology innovation tool, external system suitability control, and cross-industry harmonization platform. Graphical abstract The NISTmAb Reference Material (RM) 8671 is intended to be an industry standard monoclonal antibody for pre-competitive harmonization of best practices and designing next generation characterization technologies for identity, quality, and stability testing.

Development of an LC-MS/MS peptide mapping protocol for the NISTmAb.

Peptide mapping is a component of the analytical toolbox used within the biopharmaceutical industry to aid in the identity confirmation of a protein therapeutic and to monitor degradative events such as oxidation or deamidation. These methods offer the advantage of providing site-specific information regarding post-translational and chemical modifications that may arise during production, processing or storage. A number of such variations may also be induced by the sample preparation methods themselves which may confound the ability to accurately evaluate the true modification levels. One important focus when developing a peptide mapping method should therefore be the use of sample preparation conditions that will minimize the degree of artificial modifications induced. Unfortunately, the conditions that are amenable to effective reduction, alkylation and digestion are often the same conditions that promote unwanted modifications. Here we describe the optimization of a tryptic digestion protocol used for peptide mapping of the NISTmAb IgG1κ which addresses the challenge of balancing maximum digestion efficiency with minimum artificial modifications. The parameters on which we focused include buffer concentration, digestion time and temperature, as well as the source and type of trypsin (recombinant vs. pancreatic; bovine vs porcine) used. Using the optimized protocol we generated a peptide map of the NISTmAb which allowed us to confirm its identity at the level of primary structure. Graphical abstract Peptide map of the NISTmAb RM 8671 monoclonal antibody. Tryptic digestion was performed using an optimized protocol and followed by LC-UV-MS analysis. The trace represents the total ion chromatogram. Each peak was mapped to peptides identified using mass spectrometry data.

A systematic evaluation of normalization methods in quantitative label-free proteomics.

To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.

The C-score: a Bayesian framework to sharply improve proteoform scoring in high-throughput top down proteomics.

The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the "C-score" (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, "off-by-1 Da" discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used within both ProSightPC and ProSightPTM on a manually curated set of 295 human proteoforms. The current implementation of the C-score framework generated a marked improvement over the existing scoring system as measured by the area under the curve on the resulting ROC chart (AUC of 0.99 versus 0.78).

Serum peptide mapping in gastric precancerous lesion and cancer.

OBJECTIVE: To construct and verify a diagnostic model using proteomic analysis of serum samples for identifying gastric precancerous lesions and gastric cancer (GC). METHODS: The serum samples from 25 patients with gastric precancerous lesions (chronic atrophic gastritis with mild to moderate dysplasia), 25 GC patients and 25 healthy controls were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Spectral peaks with significant difference among the groups were identified and used as a diagnostic model for detecting gastric precancerous lesions and GC. The serum peptide map model was validated using an independent sample set including 15 healthy volunteers, 15 precancerous and 15 GC patients. RESULTS: The spectral peaks for the peptides with mass-to-charge (m/z) values of 1741 and 4210 were the most significantly different among the three groups. The sensitivity of this diagnostic model for detecting healthy controls, patients with gastric precancerous lesions and patients with GC was 80.0% (12/15), 66.7% (10/15) and 66.7% (10/15) respectively, while the specificity was 66.7% (20/30), 73.3% (22/30) and 73.3% (22/30), respectively. CONCLUSION: Our diagnostic model is useful for diagnosing gastric precancerous lesions and GC.

IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification.

Differentiating and quantifying protein differences in complex samples produces significant challenges in sensitivity and specificity. Label-free quantification can draw from two different information sources: precursor intensities and spectral counts. Intensities are accurate for calculating protein relative abundance, but values are often missing due to peptides that are identified sporadically. Spectral counting can reliably reproduce difference lists, but differentiating peptides or quantifying all but the most concentrated protein changes is usually beyond its abilities. Here we developed new software, IDPQuantify, to align multiple replicates using principal component analysis, extract accurate precursor intensities from MS data, and combine intensities with spectral counts for significant gains in differentiation and quantification. We have applied IDPQuantify to three comparative proteomic data sets featuring gold standard protein differences spiked in complicated backgrounds. The software is able to associate peptides with peaks that are otherwise left unidentified to increase the efficiency of protein quantification, especially for low-abundance proteins. By combing intensities with spectral counts from IDPicker, it gains an average of 30% more true positive differences among top differential proteins. IDPQuantify quantifies protein relative abundance accurately in these test data sets to produce good correlations between known and measured concentrations.

Combination of gas-phase fractionation and MS³ acquisition modes for relative protein quantification with isobaric tagging.

Relative quantification of peptides and proteins with isobaric tags such as iTRAQ or TMT is commonly used in comparative quantitative proteomics based on tandem mass spectrometry (MS/MS). Nonetheless, isobaric tagging inherently suffers from the cofragmentation/interference phenomenon that may compromise the quality of the quantitative data. An MS(3) acquisition mode has been recently proposed to address this issue. Because of the additional ion isolation and fragmentation step, the MS(3) acquisition mode significantly alleviates this interference effect. However, MS(3) acquisition exhibits a lower sensitivity and a higher duty cycle, both of which reduce the number of identified and quantified proteins. In the present study, we evaluated the combination of gas-phase fractionation (GPF) and MS(3) acquisition modes to optimize both identification and quantification of tryptic peptides labeled with TMT using a hybrid ion trap-orbitrap (LTQ-OT) instrument. An interference model was used where TMT-labeled human plasma proteolytic digests were spiked with TMT-labeled E. coli proteolytic digests. When combined with GPF, the MS(3) acquisition mode was compared with MS(2) modes such as high-energy collision dissociation (HCD) and combined collision-induced dissociation (CID)/HCD. We demonstrated the benefit of using both GPF and MS(3) to analyze tryptic peptides labeled with TMT in terms of quantification precision and accuracy as well as proteome coverage. We further explored parameters such as the influence of automatic gain control and additional MS(3) scans. The TMT-GPF-MS(3) workflow was shown to be a powerful alternative for quantitative proteomic studies that offers improved identification/quantification accuracy and enhanced proteome coverage without the need for extensive sample fractionation before MS analysis.

Integral quantification accuracy estimation for reporter ion-based quantitative proteomics (iQuARI).

With the increasing popularity of comparative studies of complex proteomes, reporter ion-based quantification methods such as iTRAQ and TMT have become commonplace in biological studies. Their appeal derives from simple multiplexing and quantification of several samples at reasonable cost. This advantage yet comes with a known shortcoming: precursors of different species can interfere, thus reducing the quantification accuracy. Recently, two methods were brought to the community alleviating the amount of interference via novel experimental design. Before considering setting up a new workflow, tuning the system, optimizing identification and quantification rates, etc. one legitimately asks: is it really worth the effort, time and money? The question is actually not easy to answer since the interference is heavily sample and system dependent. Moreover, there was to date no method allowing the inline estimation of error rates for reporter quantification. We therefore introduce a method called iQuARI to compute false discovery rates for reporter ion based quantification experiments as easily as Target/Decoy FDR for identification. With it, the scientist can accurately estimate the amount of interference in his sample on his system and eventually consider removing shadows subsequently, a task for which reporter ion quantification might not be the solution of choice.

Determination of ¹5N-incorporation into plant proteins and their absolute quantitation: a new tool to study nitrogen flux dynamics and protein pool sizes elicited by plant-herbivore interactions.

Herbivory leads to changes in the allocation of nitrogen among different pools and tissues; however, a detailed quantitative analysis of these changes has been lacking. Here, we demonstrate that a mass spectrometric data-independent acquisition approach known as LC-MS(E), combined with a novel algorithm to quantify heavy atom enrichment in peptides, is able to quantify elicited changes in protein amounts and (15)N flux in a high throughput manner. The reliable identification/quantitation of rabbit phosphorylase b protein spiked into leaf protein extract was achieved. The linear dynamic range, reproducibility of technical and biological replicates, and differences between measured and expected (15)N-incorporation into the small (SSU) and large (LSU) subunits of ribulose-1,5-bisphosphate-carboxylase/oxygenase (RuBisCO) and RuBisCO activase 2 (RCA2) of Nicotiana attenuata plants grown in hydroponic culture at different known concentrations of (15)N-labeled nitrate were used to further evaluate the procedure. The utility of the method for whole-plant studies in ecologically realistic contexts was demonstrated by using (15)N-pulse protocols on plants growing in soil under unknown (15)N-incorporation levels. Additionally, we quantified the amounts of lipoxygenase 2 (LOX2) protein, an enzyme important in antiherbivore defense responses, demonstrating that the approach allows for in-depth quantitative proteomics and (15)N flux analyses of the metabolic dynamics elicited during plant-herbivore interactions.

Temperature-dependent instability of the cTnI subunit in NIST SRM2921 characterized by tryptic peptide mapping.

In this study temperature-dependent instability of the cTnI subunit of the three-protein complex NIST SRM2921 was demonstrated using a mass spectrometric tryptic peptide mapping approach. The results were compared to the cTnI subunit obtained as a protein standard from Calbiochem with identical amino acid sequence. Both the three-protein complex from NIST as well as the cTnI subunit were incubated at elevated temperatures and then evaluated with respect to the primary sequence. The corresponding peptide maps were analyzed using LC-MS/MS. From a Mascot database search in combination with "semiTrypsin" tolerance it was found that two peptide backbone cleavages had occurred in subunit cTnI in NIST SRM2921 material upon incubation at 37°C, namely between amino acids at 148/149 and 194/195. The Calbiochem standard did not show increased levels of "unexpected" peptides in tryptic peptide maps. One of the two peptide backbone cleavages could also be monitored using a "single-step" MALDI-MS approach, i.e. without the need for peptide separation. The amount of degradation appeared rather constant in replicate temperature-instability experiments. However, for accurate quantification internal labelled standards are needed.

Automated selected reaction monitoring software for accurate label-free protein quantification.

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

Internal calibrants allow high accuracy peptide matching between MALDI imaging MS and LC-MS/MS.

One of the important challenges for MALDI imaging mass spectrometry (MALDI-IMS) is the unambiguous identification of measured analytes. One way to do this is to match tryptic peptide MALDI-IMS m/z values with LC-MS/MS identified m/z values. Matching using current MALDI-TOF/TOF MS instruments is difficult due to the variability of in situ time-of-flight (TOF) m/z measurements. This variability is currently addressed using external calibration, which limits achievable mass accuracy for MALDI-IMS and makes it difficult to match these data to downstream LC-MS/MS results. To overcome this challenge, the work presented here details a method for internally calibrating data sets generated from tryptic peptide MALDI-IMS on formalin-fixed paraffin-embedded sections of ovarian cancer. By calibrating all spectra to internal peak features the m/z error for matches made between MALDI-IMS m/z values and LC-MS/MS identified peptide m/z values was significantly reduced. This improvement was confirmed by follow up matching of LC-MS/MS spectra to in situ MS/MS spectra from the same m/z peak features. The sum of the data presented here indicates that internal calibrants should be a standard component of tryptic peptide MALDI-IMS experiments.

A complex standard for protein identification, designed by evolution.

Shotgun proteomic investigations rely on the algorithmic assignment of mass spectra to peptides. The quality of these matches is therefore a cornerstone in the analysis and has been the subject of numerous recent developments. In order to establish the benefits of novel algorithms, they are applied to reference samples of known content. However, these were recently shown to be either too simple to resemble typical real-life samples or as leading to results of lower accuracy as the method itself. Here, we describe how to use the proteome of Pyrococcus furiosus , a hyperthermophile, as a standard to evaluate proteomics identification workflows. Indeed, we prove that the Pyrococcus furiosus proteome provides a valid method for detecting random hits, comparable to the decoy databases currently in popular use, but we also prove that the Pyrococcus furiosus proteome goes squarely beyond the decoy approach by also providing many hundreds of highly reliable true positive hits. Searching the Pyrococcus furiosus proteome can thus be used as a unique test that provides the ability to reliably detect both false positives as well as proteome-scale true positives, allowing the rigorous testing of identification algorithms at the peptide and protein level.

Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification.

Fourier transform-all reaction monitoring (FT-ARM) is a novel approach for the identification and quantification of peptides that relies upon the selectivity of high mass accuracy data and the specificity of peptide fragmentation patterns. An FT-ARM experiment involves continuous, data-independent, high mass accuracy MS/MS acquisition spanning a defined m/z range. Custom software was developed to search peptides against the multiplexed fragmentation spectra by comparing theoretical or empirical fragment ions against every fragmentation spectrum across the entire acquisition. A dot product score is calculated against each spectrum to generate a score chromatogram used for both identification and quantification. Chromatographic elution profile characteristics are not used to cluster precursor peptide signals to their respective fragment ions. FT-ARM identifications are demonstrated to be complementary to conventional data-dependent shotgun analysis, especially in cases where the data-dependent method fails because of fragmenting multiple overlapping precursors. The sensitivity, robustness, and specificity of FT-ARM quantification are shown to be analogous to selected reaction monitoring-based peptide quantification with the added benefit of minimal assay development. Thus, FT-ARM is demonstrated to be a novel and complementary data acquisition, identification, and quantification method for the large scale analysis of peptides.

Partially sequenced organisms, decoy searches and false discovery rates.

Tandem mass spectrometry is commonly used to identify peptides, typically by comparing their product ion spectra with those predicted from a protein sequence database and scoring these matches. The most reported quality metric for a set of peptide identifications is the false discovery rate (FDR), the fraction of expected false identifications in the set. This metric has so far only been used for completely sequenced organisms or known protein mixtures. We have investigated whether FDR estimations are also applicable in the case of partially sequenced organisms, where many high-quality spectra fail to identify the correct peptides because the latter are not present in the searched sequence database. Using real data from human plasma and simulated partial sequence databases derived from two complete human sequence databases with different levels of redundancy, we could demonstrate that the mixture model approach in PeptideProphet is robust for partial databases, particularly if used in combination with decoy sequences. We therefore recommend using this method when estimating the FDR and reporting peptide identifications from incompletely sequenced organisms.

Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.

Most proteomic studies use liquid chromatography coupled to tandem mass spectrometry to identify and quantify the peptides generated by the proteolysis of a biological sample. However, with the current methods it remains challenging to rapidly, consistently, reproducibly, accurately, and sensitively detect and quantify large fractions of proteomes across multiple samples. Here we present a new strategy that systematically queries sample sets for the presence and quantity of essentially any protein of interest. It consists of using the information available in fragment ion spectral libraries to mine the complete fragment ion maps generated using a data-independent acquisition method. For this study, the data were acquired on a fast, high resolution quadrupole-quadrupole time-of-flight (TOF) instrument by repeatedly cycling through 32 consecutive 25-Da precursor isolation windows (swaths). This SWATH MS acquisition setup generates, in a single sample injection, time-resolved fragment ion spectra for all the analytes detectable within the 400-1200 m/z precursor range and the user-defined retention time window. We show that suitable combinations of fragment ions extracted from these data sets are sufficiently specific to confidently identify query peptides over a dynamic range of 4 orders of magnitude, even if the precursors of the queried peptides are not detectable in the survey scans. We also show that queried peptides are quantified with a consistency and accuracy comparable with that of selected reaction monitoring, the gold standard proteomic quantification method. Moreover, targeted data extraction enables ad libitum quantification refinement and dynamic extension of protein probing by iterative re-mining of the once-and-forever acquired data sets. This combination of unbiased, broad range precursor ion fragmentation and targeted data extraction alleviates most constraints of present proteomic methods and should be equally applicable to the comprehensive analysis of other classes of analytes, beyond proteomics.

Quantotypic properties of QconCAT peptides targeting bovine host response to Streptococcus uberis.

Mammalian host response to pathogens is associated with fluctuations in high abundant proteins in body fluids as well as in regulation of proteins expressed in relatively low copy numbers like cytokines secreted from immune cells and endothelium. Hence, efficient monitoring of proteins associated with host response to pathogens remains a challenging task. In this paper, we present a targeted proteome analysis of a panel of 20 proteins that are widely believed to be key players and indicators of bovine host response to mastitis pathogens. Stable isotope-labeled variants of two concordant proteotypic peptides from each of these 20 proteins were obtained through the QconCAT method. We present the quantotypic properties of these 40 proteotypic peptides and discuss their application to research in host-pathogen interactions. Our results clearly demonstrate a robust monitoring of 17 targeted host-response proteins. Twelve of these were readily quantified in a simple extraction of mammary gland tissues, while the expression levels of the remaining proteins were too low for direct and stable quantification; hence, their accurate quantification requires further fractionation of mammary gland tissues.

BuildSummary: using a group-based approach to improve the sensitivity of peptide/protein identification in shotgun proteomics.

The target-decoy database search strategy is widely accepted as a standard method for estimating the false discovery rate (FDR) of peptide identification, based on which peptide-spectrum matches (PSMs) from the target database are filtered. To improve the sensitivity of protein identification given a fixed accuracy (frequently defined by a protein FDR threshold), a postprocessing procedure is often used that integrates results from different peptide search engines that had assayed the same data set. In this work, we show that PSMs that are grouped by the precursor charge, the number of missed internal cleavage sites, the modification state, and the numbers of protease termini and that the proteins grouped by their unique peptide count should be filtered separately according to the given FDR. We also develop an iterative procedure to filter the PSMs and proteins simultaneously, according to the given FDR. Finally, we present a general framework to integrate the results from different peptide search engines using the same FDR threshold. Our method was tested with several shotgun proteomics data sets that were acquired by multiple LC/MS instruments from two different biological samples. The results showed a satisfactory performance. We implemented the method in a user-friendly software package called BuildSummary, which can be downloaded for free from http://www.proteomics.ac.cn/software/proteomicstools/index.htm as part of the software suite ProteomicsTools.

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment.

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.