A large synthetic peptide and phosphopeptide reference library for mass spectrometry–based proteomics

A universal molecular control for DNA, mRNA and protein expression

The expression of genes encompasses their transcription into mRNA followed by translation into protein. In recent years, next-generation sequencing and mass spectrometry methods have profiled DNA, RNA and protein abundance in cells. However, there are currently no reference standards that are compatible across these genomic, transcriptomic and proteomic methods, and provide an integrated measure of gene expression. Here, we use synthetic biology principles to engineer a multi-omics control, termed pREF, that can act as a universal molecular standard for next-generation sequencing and mass spectrometry methods. The pREF sequence encodes 21 synthetic genes that can be in vitro transcribed into spike-in mRNA controls, and in vitro translated to generate matched protein controls. The synthetic genes provide qualitative controls that can measure sensitivity and quantitative accuracy of DNA, RNA and peptide detection. We demonstrate the use of pREF in metagenome DNA sequencing and RNA sequencing experiments and evaluate the quantification of proteins using mass spectrometry. Unlike previous spike-in controls, pREF can be independently propagated and the synthetic mRNA and protein controls can be sustainably prepared by recipient laboratories using common molecular biology techniques. Together, this provides a universal synthetic standard able to integrate genomic, transcriptomic and proteomic methods.

Retention time prediction for post-translationally modified peptides: Ser, Thr, Tyr-phosphorylation

The development of a peptide retention prediction model for reversed-phase chromatography applications in proteomics is reported for peptides carrying phosphorylated Ser, Thr and Tyr-residues. The major retention features have been assessed using a collection of over 10,000 phosphorylated/non-phosphorylated peptide pairs identified in a series 1D and 2D LC-MS/MS acquisitions using formic acid as ion pairing modifier. Single modification event on average results in increased peptide retention for phosphorylation of Ser (+ 1.46), Thr (+1.33), Tyr (+0.93% acetonitrile, ACN) on gradient elution scale for Luna C18(2) stationary phase. We established several composition and sequence specific features, which drive deviations from these average values. Thus, single phosphorylation of serine results in retention shifts ranging from -2.4 to 5.5% ACN depending on position of the residue, nature of nearest neighbour residues, peptide length, hydrophobicity and pI value, and its propensity to form amphipathic helical structures. We established that the altered ion-pairing environment upon phosphorylation is detrimental for this variability. Hydrophobicity of ion-pairing modifier directly informs the magnitude of expected shifts: (most hydrophilic) 0.5 % acetic acid (larger positive shift upon phosphorylation) > 0.1 % formic acid (positive) > 0.1 % trifluoroacetic (negative) > 0.1 % heptafluorobutyric acid (larger negative shift). The effect of phosphorylation has been also evaluated for several separation conditions used in the first dimension of 2D LC applications: high pH reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), strong cation- and strong anion exchange separations.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics

The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.

De Novo Sequencing of Synthetic Bis-cysteine Peptide Macrocycles Enabled by "Chemical Linearization" of Compound Mixtures

A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on bis-Cys peptides cross-linked with the m-xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.

Compendium of Chromatographic Behavior of Post-translationally and Chemically Modified Peptides in Bottom-Up Proteomic Experiments

We have systematically evaluated the chromatographic behavior of post-translationally/chemically modified peptides using data spanning over 70 of the most relevant modifications. These retention properties were measured for standard bottom-up proteomic settings (fully porous C18 separation media, 0.1% formic acid as ion-pairing modifier) using collections of modified/nonmodified peptide pairs. These pairs were generated by spontaneous degradation, chemical or enzymatic treatment, analysis of synthetic peptides, or the cotranslational incorporation of noncanonical proline analogues. In addition, these measurements were validated using external data acquired for synthetic peptides and enzymatically induced citrullination. Working in units of hydrophobicity index (HI, % ACN) and evaluating the average retention shifts (ΔHI) represent the simplest approach to describe the effect of modifications from a didactic point of view. Plotting HI values for modified (y-axis) vs nonmodified (x-axis) counterparts generates unique slope and intercept values for each modification defined by the chemistry of the modifying moiety: its hydrophobicity, size, pKa of ionizable groups, and position of the altered residue. These composition-dependent correlations can be used for coarse incorporation of PTMs into models for prediction of peptide retention. More accurate predictions would require the development of specific sequence-dependent algorithms to predict ΔHI values.

A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

DeepFLR facilitates false localization rate control in phosphoproteomics

Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data

Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Prediction of peptide mass spectral libraries with machine learning

The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.

A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics

Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.

Data-independent acquisition proteomics methods for analyzing post-translational modifications

Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, i.e., detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA-based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in-depth and accurate PTMs measurements. This article is protected by copyright. All rights reserved.

Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry

Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.

Method for Independent Estimation of the False Localization Rate for Phosphoproteomics

Phosphoproteomic methods are commonly employed to identify and quantify phosphorylation sites on proteins. In recent years, various tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified or to estimate the global false localization rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic datasets, and their statistical reliability on real datasets is largely unknown, potentially leading to studies reporting incorrectly localized phosphosites, due to inadequate statistical control. In this work, we develop the concept of scoring modifications on a decoy amino acid, that is, one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of amino acids, on both synthetic and real data sets, demonstrating that the selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys. We propose the use of a decoy amino acid to control false reporting in the literature and in public databases that re-distribute the data. Data are available via ProteomeXchange with identifier PXD028840.

Widespread Arginine Phosphorylation in Staphylococcus aureus

Arginine phosphorylation was only recently discovered to play a significant and relevant role in the Gram-positive bacterium Bacillus subtilis. In addition, arginine phosphorylation was also detected in Staphylococcus aureus, suggesting a widespread role in bacteria. However, the large-scale analysis of protein phosphorylation, and especially those that involve a phosphoramidate bond, comes along with several challenges. The substoichiometric nature of protein phosphorylation requires proper enrichment strategies prior to LC-MS/MS analysis, and the acid instability of phosphoramidates was long thought to impede those enrichments. Furthermore, good spectral quality is required, which can be impeded by the presence of neutral losses of phosphoric acid upon higher energy collision–induced dissociation. Here we show that pArg is stable enough for commonly used Fe³⁺-IMAC enrichment followed by LC-MS/MS and that HCD is still the gold standard for the analysis of phosphopeptides. By profiling a serine/threonine kinase (Stk1) and phosphatase (Stp1) mutant from a methicillin-resistant S. aureus mutant library, we identified 1062 pArg sites and thus the most comprehensive arginine phosphoproteome to date. Using synthetic arginine phosphorylated peptides, we validated the presence and localization of arginine phosphorylation in S. aureus. Finally, we could show that the knockdown of Stp1 significantly increases the overall amount of arginine phosphorylation in S. aureus. However, our analysis also shows that Stp1 is not a direct protein-arginine phosphatase but only indirectly influences the arginine phosphoproteome.

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Extensive protein arginine phosphorylation in Staphylococcus aureus.

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pArg phosphorylation is stable under common phosphor enrichment conditions.

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Arginine phosphorylation is as widespread as threonine phosphorylation.

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Phosphatase Stp1 indirectly influences the pArg phosphoproteome in S. aureus.

SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.

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Spectrum clustering enables peptide identification transfer between LC–MS/MS runs.

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The SIMSI pipeline supports processing full proteome and phosphoproteome data.

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SIMSI increases the number of quantifiable PSMs by 26 to 45%.

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SIMSI reduces missing values in multibatch TMT labeling experiments by up to 21%.

Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides

Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.

Posttranslational modification of the RHO of plants protein RACB by phosphorylation and cross-kingdom conserved ubiquitination

Small RHO-type G-proteins act as signaling hubs and master regulators of polarity in eukaryotic cells. Their activity is tightly controlled, as defective RHO signaling leads to aberrant growth and developmental defects. Two major processes regulate G-protein activity: canonical shuttling between different nucleotide bound states and posttranslational modification (PTM), of which the latter can support or suppress RHO signaling, depending on the individual PTM. In plants, regulation of Rho of plants (ROPs) signaling activity has been shown to act through nucleotide exchange and GTP hydrolysis, as well as through lipid modification, but there is little data available on phosphorylation or ubiquitination of ROPs. Hence, we applied proteomic analyses to identify PTMs of the barley ROP RACB. We observed in vitro phosphorylation by barley ROP binding kinase 1 and in vivo ubiquitination of RACB. Comparative analyses of the newly identified RACB phosphosites and human RHO protein phosphosites revealed conservation of modified amino acid residues, but no overlap of actual phosphorylation patterns. However, the identified RACB ubiquitination site is conserved in all ROPs from Hordeum vulgare, Arabidopsis thaliana and Oryza sativa and in mammalian Rac1 and Rac3. Point mutation of this ubiquitination site leads to stabilization of RACB. Hence, this highly conserved lysine residue may regulate protein stability across different kingdoms.

Affinity Selection from Synthetic Peptide Libraries Enabled by De Novo MS/MS Sequencing

Recently, de novo MS/MS peptide sequencing has enabled the application of affinity selections to synthetic peptide mixtures that approach the diversity of phage libraries (> 108 random peptides). In conjunction with ‘split-mix’ solid phase synthesis to access equimolar peptide mixtures, this approach provides a straightforward means to examine synthetic peptide libraries of considerably higher diversity than has been feasible historically. Here, we offer a critical perspective on this work, report emerging data, and highlight opportunities for further methods refinement. With continued development, ‘affinity selection–mass spectrometry’ may become a complimentary approach to phage display, in vitro selection, and DNA-encoded libraries for the discovery of synthetic ligands that modulate protein function.

Hierarchically mesoporous Ce-based MOFs with enhanced alkaline phosphatase-like activity for phosphorylated biomarker sensing

We developed a hierarchically mesoporous metal–organic framework nanozyme with enhanced alkaline phosphatase-mimicking activity for rapid and sensitive sensing of phosphorylated biomarkers.

PLDMS: Phosphopeptide Library Dephosphorylation Followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences

A detailed understanding of the sequence preference surrounding phosphorylation sites is essential for deciphering the function of the human phosphoproteome . Whereas the mechanisms for substrate site recognition by kinases are relatively well understood, the selection mechanisms for the corresponding phosphatases pose several obstacles. However, multiple pieces of evidence point towards a role of the amino acid sequence in the direct vicinity of the phosphorylation site for recognition by phosphatase enzymes. Peptide library-based studies for enzymes attaching posttranslational modifications (PTMs) are relatively straight forward to carry out. However, studying enzymes removing PTMs pose a challenge in that libraries with a PTM attached are needed as a starting point. Here, we present our methodology using large synthetic phosphopeptide libraries to study the preferred sequence context of protein phosphatases. The approach, termed "phosphopeptide library dephosphorylation followed by mass spectrometry" (PLDMS), allows for the exact control of phosphorylation site incorporation and the synthetic route is capable of covering several thousand peptides in a single tube reaction. Furthermore, it enables the user to analyze MS data tailored to the needs of a specific library and thereby increase data quality. We therefore expect a wide applicability of this technique for a range of enzymes catalyzing the removal of PTMs.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

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Computational method selection substantially affects phosphopeptide identification.

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Deep-learning-derived metrics effectively discriminate correct and incorrect PSMs.

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Novel metrics enable computational method comparison on real application data.

Decoding post translational modification crosstalk with proteomics

Post-translational modification (PTM) of proteins allows cells to regulate protein functions, transduce signals and respond to perturbations. PTMs expand protein functionality and diversity, which leads to increased proteome complexity. PTM crosstalk describes the combinatorial action of multiple PTMs on the same or on different proteins for higher order regulation. Here we review how recent advances in proteomic technologies, mass spectrometry instrumentation, and bioinformatics spurred the proteome-wide identification of PTM crosstalk through measurements of PTM sites. We provide an overview of the basic modes of PTM crosstalk, the proteomic methods to elucidate PTM crosstalk, and approaches that can inform about the functional consequences of PTM crosstalk.

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Description of basic modules and different modes of PTM crosstalk.

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Overview of current proteomic methods to identify and infer PTM crosstalk.

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Discussion of large-scale approaches to characterize functional PTM crosstalk.

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Future directions and potential proteomic methods for elucidating PTM crosstalk.

P38α-MAPK phosphorylates Snapin and reduces Snapin-mediated BACE1 transportation in APP-transgenic mice

Amyloid β peptide (Aβ) is the major pathogenic molecule in Alzheimer's disease (AD). BACE1 enzyme is essential for the generation of Aβ. Deficiency of p38α-MAPK in neurons increases lysosomal degradation of BACE1 and decreases Aβ deposition in the brain of APP-transgenic mice. However, the mechanisms mediating effects of p38α-MAPK are largely unknown. In this study, we used APP-transgenic mice and cultured neurons and observed that deletion of p38α-MAPK specifically in neurons decreased phosphorylation of Snapin at serine, increased retrograde transportation of BACE1 in axons and reduced BACE1 at synaptic terminals, which suggests that p38α-MAPK deficiency promotes axonal transportation of BACE1 from its predominant locations, axonal terminals, to lysosomes in the cell body. In vitro kinase assay revealed that p38α-MAPK directly phosphorylates Snapin. By further performing mass spectrometry analysis and site-directed mutagenic experiments in SH-SY5Y cell lines, we identified serine residue 112 as a p38α-MAPK-phosphorylating site on Snapin. Replacement of serine 112 with alanine did abolish p38α-MAPK knockdown-induced reduction of BACE1 activity and protein level, and transportation to lysosomes in SH-SY5Y cells. Taken together, our study suggests that activation of p38α-MAPK phosphorylates Snapin and inhibits the retrograde transportation of BACE1 in axons, which might exaggerate amyloid pathology in AD brain.

Exploring the diversity of plant proteome

The tremendous functional, spatial, and temporal diversity of the plant proteome is regulated by multiple factors that continuously modify protein abundance, modifications, interactions, localization, and activity to meet the dynamic needs of plants. Dissecting the proteome complexity and its underlying genetic variation is attracting increasing research attention. Mass spectrometry (MS)-based proteomics has become a powerful approach in the global study of protein functions and their relationships on a systems level. Here, we review recent breakthroughs and strategies adopted to unravel the diversity of the proteome, with a specific focus on the methods used to analyze posttranslational modifications (PTMs), protein localization, and the organization of proteins into functional modules. We also consider PTM crosstalk and multiple PTMs temporally regulating the life cycle of proteins. Finally, we discuss recent quantitative studies using MS to measure protein turnover rates and examine future directions in the study of the plant proteome.

Effect of Phosphorylation on the Collision Cross Sections of Peptide Ions in Ion Mobility Spectrometry

The insertion of ion mobility spectrometry (IMS) between LC and MS can improve peptide identification in both proteomics and phosphoproteomics by providing structural information that is complementary to LC and MS, because IMS separates ions on the basis of differences in their shapes and charge states. However, it is necessary to know how phosphate groups affect the peptide collision cross sections (CCS) in order to accurately predict phosphopeptide CCS values and to maximize the usefulness of IMS. In this work, we systematically characterized the CCS values of 4,433 pairs of mono-phosphopeptide and corresponding unphosphorylated peptide ions using trapped ion mobility spectrometry (TIMS). Nearly one-third of the mono-phosphopeptide ions evaluated here showed smaller CCS values than their unphosphorylated counterparts, even though phosphorylation results in a mass increase of 80 Da. Significant changes of CCS upon phosphorylation occurred mainly in structurally extended peptides with large numbers of basic groups, possibly reflecting intramolecular interactions between phosphate and basic groups.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .

A Fast and Memory-Efficient Spectral Library Search Algorithm Using Locality-Sensitive Hashing

With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

We report O-Pair Search, a new approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides using an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus.

Proteomic and transcriptomic profiling of aerial organ development in Arabidopsis

Plant growth and development are regulated by a tightly controlled interplay between cell division, cell expansion and cell differentiation during the entire plant life cycle from seed germination to maturity and seed propagation. To explore some of the underlying molecular mechanisms in more detail, we selected different aerial tissue types of the model plant Arabidopsis thaliana, namely rosette leaf, flower and silique/seed and performed proteomic, phosphoproteomic and transcriptomic analyses of sequential growth stages using tandem mass tag-based mass spectrometry and RNA sequencing. With this exploratory multi-omics dataset, development dynamics of photosynthetic tissues can be investigated from different angles. As expected, we found progressive global expression changes between growth stages for all three omics types and often but not always corresponding expression patterns for individual genes on transcript, protein and phosphorylation site level. The biggest difference between proteomic- and transcriptomic-based expression information could be observed for seed samples. Proteomic and transcriptomic data is available via ProteomeXchange and ArrayExpress with the respective identifiers PXD018814 and E-MTAB-7978.

Dissecting the sequence determinants for dephosphorylation by the catalytic subunits of phosphatases PP1 and PP2A

The phosphatases PP1 and PP2A are responsible for the majority of dephosphorylation reactions on phosphoserine (pSer) and phosphothreonine (pThr), and are involved in virtually all cellular processes and numerous diseases. The catalytic subunits exist in cells in form of holoenzymes, which impart substrate specificity. The contribution of the catalytic subunits to the recognition of substrates is unclear. By developing a phosphopeptide library approach and a phosphoproteomic assay, we demonstrate that the specificity of PP1 and PP2A holoenzymes towards pThr and of PP1 for basic motifs adjacent to the phosphorylation site are due to intrinsic properties of the catalytic subunits. Thus, we dissect this amino acid specificity of the catalytic subunits from the contribution of regulatory proteins. Furthermore, our approach enables discovering a role for PP1 as regulator of the GRB-associated-binding protein 2 (GAB2)/14-3-3 complex. Beyond this, we expect that this approach is broadly applicable to detect enzyme-substrate recognition preferences.

The substrate specificity of phosphoprotein phosphatases PP1 and PP2A depends on their catalytic and regulatory subunits. Using proteomics approaches, the authors here provide insights into the sequence specificity of the catalytic subunits and their distinct contributions to PP1 and PP2A selectivity.

Resolution of NASH and hepatic fibrosis by the GLP-1R/GcgR dual-agonist Cotadutide via modulating mitochondrial function and lipogenesis

Non-alcoholic fatty liver disease and steatohepatitis are highly associated with obesity and type 2 diabetes mellitus. Cotadutide, a GLP-1R/GcgR agonist, was shown to reduce blood glycemia, body weight and hepatic steatosis in patients with T2DM. Here, we demonstrate that the effects of Cotadutide to reduce body weight, food intake and improve glucose control are predominantly mediated through the GLP-1 signaling, while, its action on the liver to reduce lipid content, drive glycogen flux and improve mitochondrial turnover and function are directly mediated through Gcg signaling. This was confirmed by the identification of phosphorylation sites on key lipogenic and glucose metabolism enzymes in liver of mice treated with Cotadutide. Complementary metabolomic and transcriptomic analyses implicated lipogenic, fibrotic and inflammatory pathways, which are consistent with a unique therapeutic contribution of GcgR agonism by Cotadutide in vivo. Significantly, Cotadutide also alleviated fibrosis to a greater extent than Liraglutide or Obeticholic acid (OCA), despite adjusting dose to achieve similar weight loss in 2 preclinical mouse models of NASH. Thus Cotadutide, via direct hepatic (GcgR) and extra-hepatic (GLP-1R) effects, exerts multi-factorial improvement in liver function and is a promising therapeutic option for the treatment of steatohepatitis.

The Hippo Pathway Transducers YAP1/TEAD Induce Acquired Resistance to Trastuzumab in HER2-Positive Breast Cancer

Trastuzumab is the first-line targeted therapeutic drug for HER2-positive breast cancer, leading to improved overall survival. However, acquired resistance inevitably occurs. We aimed to identify, quantify, and assess the mechanisms of acquired resistance to trastuzumab. We established an acquired trastuzumab-resistant model in vitro from BT-474, a trastuzumab-sensitive, HER2-amplified breast-cancer cell line. A multi-omic strategy was implemented to obtain gene, proteome, and phosphoproteome signatures associated with acquired resistance to trastuzumab in HER2-positive breast cancer, followed by validation in human clinical samples. YAP1 dephosphorylation and TEAD2 overexpression were detected as significant alterations in the Hippo pathway in trastuzumab-resistant breast cancer. Because of the emerging role of these proteins as mediators of normal growth and tumorigenesis, we assessed the exogenous modulation of their activity, either by in vitro gene silencing or by pharmacological inhibition of the YAP1/TEAD complexes, both in vitro and in vivo. Moreover, we identified increased signaling through the Hippo pathway in human samples after progression following trastuzumab treatment. Finally, YAP1/TAZ nuclear accumulation in malignant cells in HER2 breast tumor was significantly associated with worse progression-free and overall survival in metastatic HER2-positive breast-cancer patients. Our results suggest the involvement of Hippo signaling in acquired trastuzumab resistance in breast cancer. Additionally, we provide novel evidence for a potential breast-cancer treatment strategy based on dual targeting of HER2 and Hippo pathway effectors, which may improve the antitumor activity of trastuzumab and help overcome resistance.

Mass-spectrometry-based draft of the Arabidopsis proteome

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities¹. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.

Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization

Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

Kinase Inhibitor Treatment of Patients with Advanced Cancer Results in High Tumor Drug Concentrations and in Specific Alterations of the Tumor Phosphoproteome

Identification of predictive biomarkers for targeted therapies requires information on drug exposure at the target site as well as its effect on the signaling context of a tumor. To obtain more insight in the clinical mechanism of action of protein kinase inhibitors (PKIs), we studied tumor drug concentrations of protein kinase inhibitors (PKIs) and their effect on the tyrosine-(pTyr)-phosphoproteome in patients with advanced cancer. Tumor biopsies were obtained from 31 patients with advanced cancer before and after 2 weeks of treatment with sorafenib (SOR), erlotinib (ERL), dasatinib (DAS), vemurafenib (VEM), sunitinib (SUN) or everolimus (EVE). Tumor concentrations were determined by LC-MS/MS. pTyr-phosphoproteomics was performed by pTyr-immunoprecipitation followed by LC-MS/MS. Median tumor concentrations were 2–10 µM for SOR, ERL, DAS, SUN, EVE and >1 mM for VEM. These were 2–178 × higher than median plasma concentrations. Unsupervised hierarchical clustering of pTyr-phosphopeptide intensities revealed patient-specific clustering of pre- and on-treatment profiles. Drug-specific alterations of peptide phosphorylation was demonstrated by marginal overlap of robustly up- and downregulated phosphopeptides. These findings demonstrate that tumor drug concentrations are higher than anticipated and result in drug specific alterations of the phosphoproteome. Further development of phosphoproteomics-based personalized medicine is warranted.

A photocleavable and mass spectrometric DNA-peptide probe enables fast and specific enzyme-free detection of microRNA

MiRNAs are known to be involved in a series of diseases, including breast cancer, and they have the potential to serve as diagnostic/prognostic markers and therapeutic targets. A prerequisite for miRNAs to be applied in clinical practice is the quantitative profiling of their expression. However, the majority of current assays used in miRNA detection are highly enzyme-dependent. In this study, a novel enzyme-free assay was developed that relies on stacking hybridization and a photocleavable DNA-PL-peptide probe, which contains a reporter peptide (AVLGVDPFR), a photocleavable o-nitrobenzyl derivative linker and a detection DNA sequence that is complementary to a part of the target miRNA (e.g., miR-21, miR-125a or miR-200c). Stacking hybridization enabled the DNA-PL-peptide probe to capture DNA in a contiguous tandem arrangement to generate a long DNA single strand complementary to the target miRNA. Then, photolysis was initiated to rapidly release the reporter peptide, and the reporter peptide was ultimately monitored by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this experiment, the parameters linked with photorelease, binding, conjugation and hybridization were characterized. The results showed that the assay time was significantly shortened, and the detection specificity was improved. After validation of the assay, the target miRNA level was determined in human breast cells and tissue samples. The results demonstrated that photocleavable materials coupled with mass spectrometric detection have great potential in clinical practice.

Real-time laser induced chemical derivatizations of peptide N-Terminus for in-situ mass spectrometric sequencing at sub-picomole and nanosecond scale

Distinguishing b- and y-ions is essential to compute amino acid sequences from either N- or C-terminus in mass spectrometry. We described herein a solvent free and real time on-plate derivatization approach that can tag N-terminus of peptides at microliter level with p-chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde for matrix assisted laser desorption ionization mass spectrometry (MALDI MS). Less than 1 μL of sample solutions can be directly mixed with equal volumes of p-chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde and α-cyano-4-hydroxycinnamic acid (CHCA), a matrix compound to co-crystalize with analytes for efficient absorption of laser energy and peptide ionization. When the mixture spotted on the sample plate is irradiated with the 3rd harmonic (355 nm) of Nd³⁺:YAG laser pulses (3 ns width), N-terminal amine groups of peptides instantly react with carbonyl groups of chlorobenzaldehyde or 2-hydroxy-5-methylisophthalaldehyde. Resultant peptides carrying with on-plate formed azomethine group (-CN-) are simultaneously protonated and isolated as precursor ions for subsequent collision-activated dissociation. The mass shift with unique Cl isotopic signature unambiguously distinguishes b ions from y ions and other ions. This method does not need extensive sample preparation and is useful for those samples with limited quantities down to sub-picomole level in sub-microliter volumes. The efficiency was demonstrated with synthetic peptides and tryptic peptides of model proteins. It was found that 2-hydroxy-5-methylisophthalaldehyde provides improved yield for peptides containing lysine residues. Unknown proteins of human saliva and bovine milk as well as phosphopeptides have been identified.

Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites

Tandem mass spectrometry (MS/MS) has been used in analysis of proteins and their post-translational modifications. A recently developed data analysis method, which simulates MS/MS spectra of phosphopeptides and performs spectral library searching using SpectraST, facilitates confident localization of phosphorylation sites. However, its performance has been evaluated only on MS/MS spectra acquired using Orbitrap HCD mass spectrometers so far. In this study, we have investigated whether this approach would be applicable to another type of mass spectrometers, and optimized the simulation and search conditions to achieve sensitive and confident site localization. Synthetic phosphopeptides and enriched K562 cell phosphopeptides were analyzed using a TripleTOF 6600 mass spectrometer before and after enzymatic dephosphorylation. Dephosphorylated peptides identified by X!Tandem database searching were subjected to spectral simulation of all possible single phosphorylations using SimPhospho software. Phosphopeptides were identified and localized by SpectraST searching against a library of the simulated spectra. Although no synthetic phosphopeptide was localized at 1% false localization rate under the previous conditions, optimization of the spectral simulation and search conditions for the TripleTOF datasets achieved the localization and improved the sensitivity. Furthermore, the optimized conditions enabled sensitive localization of K562 phosphopeptides at 1% false discovery and localization rates. These results suggest that accurate phosphopeptide simulation of TripleTOF MS/MS spectra is possible and the simulated spectral libraries can be used in SpectraST searching for confident localization of phosphorylation sites.

Online porous graphic carbon chromatography coupled with tandem mass spectrometry for post-translational modification analysis

RATIONALE

Porous graphic carbon chromatography (PGC) has a different mechanism in the retention of tryptic peptides compared with reversed-phase chromatography and in this study we show that coupling PGC with tandem mass spectrometry offer advantages for the quantitation of phosphorylation stoichiometry and characterization of site-specific glycosylation.

METHODS

Digests of protein standards (horse myoglobin, bovine fetuin and β-casein) were analyzed with a capillary liquid chromatography/tandem mass spectrometry (LC/MS/MS) system by coupling an Agilent 1100 HPLC system to a Synapt G2-Si HDMS (Waters). Peptides were separated using a HyperCarb PGC column (300 μm i.d. × 100 mm) packed with 3 μm particles. MS/MS data were collected in data-dependent mode and three MS/MS scans were acquired after the full MS scan. RAW data were transformed to .mgf by PLGS (Waters) and searched against the Swissprot database by Mascot. Chromatograms and MS/MS spectra of identified compounds were extracted with Masslynx (Waters) and imported to Origin for analysis. Glycan composition and peptide sequence were manually annotated.

RESULTS

PGC/MS/MS enabled accurate quantitation of the stoichiometry of specific phosphorylation sites from β-casein by efficient separation of the phosphopeptide and its non-phosphorylated counterpart, which cannot be achieved by reversed-phase chromatography. PGC/MS/MS also enabled comprehensive characterization of protein sialoglycosylation as isomeric glycopeptides with different combinations of α2-3- and α2-6-linked sialic acids can be separated and the ratios of each combination were verified by exoglycosidase digestion.

CONCLUSIONS

PGC has demonstrated superior separation of peptides with phosphorylation and glycosylation and can be used as an alternative in the proteomic characterization of post-translational modifications (PTMs) by polar groups.

Gaining Confidence in the Elusive Histidine Phosphoproteome

Recent technological advances have made it possible to investigate the hitherto rather elusive protein histidine phosphorylation. However, confident site-specific localization of protein histidine phosphorylation remains challenging. Here, we address this problem, presenting a mass-spectrometry-based approach that outperforms classical HCD fragmentation without compromising sensitivity. We use the phosphohistidine immonium ion as a diagnostic tool as well as ETD-based fragmentation techniques to achieve unambiguous identification and localization of histidine-phosphorylation sites. The work presented here will allow more confident investigation of the phosphohistidine proteome to reveal the roles of histidine phosphorylation in cellular signaling events.

Synthetic phosphopeptides: From spike-in standards to affinity tools for protein-protein interaction studies

Synthetic isotope labeled phosphopeptides are valuable tools for the quantification and validation of phosphoproteome data. Here, we report that the same set of phosphopeptides, which are used as spike-in standards, can be successfully applied for identification of stimulus specific protein-protein interactions mediated by the respective phosphorylation sites. As a proof-of-concept, binding of two γH2AX (pS139) phosphosite specific interaction partners, MDC1 and 53BP1, was confirmed and elevated binding affinity was revealed in response to ionizing radiation. Our strategy is generally applicable and enables multiplexed validation and functional analysis of phosphorylation sites offering great potential for the follow-up of phosphoproteome studies.