PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring, and Collision Energy Calibration

Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Test-Time Training for Deep MS/MS Spectrum Prediction Improves Peptide Identification

In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, enabling the calculation of similarity scores between the predicted and experimental spectra for peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard data sets and directly employs the model on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To tackle this problem, we developed a test-time training paradigm that adapts the pretrained model to generate experimental data-specific models, namely, PepT3. PepT3 yields a 10-40% increase in peptide identification depending on the variability in training and experimental data. Intriguingly, when applied to a patient-derived immunopeptidomic sample, PepT3 increases the identification of tumor-specific immunopeptide candidates by 60%. Two-thirds of the newly identified candidates are predicted to bind to the patient's human leukocyte antigen isoforms. To facilitate access of the model and all the results, we have archived all the intermediate files in Zenodo.org with identifier 8231084.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

Deep Learning-Assisted Analysis of Immunopeptidomics Data

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia

Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N = 276) compared to non-purified CSF (N = 169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

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.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

A pharmacoproteomic landscape of organotypic intervention responses in Gram-negative sepsis

Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events remain unclear. This knowledge gap has resulted in ineffective biomarker development and suboptimal treatment regimens to prevent and manage organ dysfunction/damage. Here, we used pharmacoproteomics to score time-dependent treatment impact in a murine Escherichia coli sepsis model after administering beta-lactam antibiotic meropenem (Mem) and/or the immunomodulatory glucocorticoid methylprednisolone (Gcc). Three distinct proteome response patterns were identified, which depended on the underlying proteotype for each organ. Gcc enhanced some positive proteome responses of Mem, including superior reduction of the inflammatory response in kidneys and partial restoration of sepsis-induced metabolic dysfunction. Mem introduced sepsis-independent perturbations in the mitochondrial proteome that Gcc counteracted. We provide a strategy for the quantitative and organotypic assessment of treatment effects of candidate therapies in relationship to dosing, timing, and potential synergistic intervention combinations during sepsis.

Scribe: Next Generation Library Searching for DDA Experiments

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

Getting Ready for Large-Scale Proteomics in Crop Plants

Plants are an indispensable cornerstone of sustainable global food supply. While immense progress has been made in decoding the genomes of crops in recent decades, the composition of their proteomes, the entirety of all expressed proteins of a species, is virtually unknown. In contrast to the model plant Arabidopsis thaliana, proteomic analyses of crop plants have often been hindered by the presence of extreme concentrations of secondary metabolites such as pigments, phenolic compounds, lipids, carbohydrates or terpenes. As a consequence, crop proteomic experiments have, thus far, required individually optimized protein extraction protocols to obtain samples of acceptable quality for downstream analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS). In this article, we present a universal protein extraction protocol originally developed for gel-based experiments and combined it with an automated single-pot solid-phase-enhanced sample preparation (SP3) protocol on a liquid handling robot to prepare high-quality samples for proteomic analysis of crop plants. We also report an automated offline peptide separation protocol and optimized micro-LC-MS/MS conditions that enables the identification and quantification of ~10,000 proteins from plant tissue within 6 h of instrument time. We illustrate the utility of the workflow by analyzing the proteomes of mature tomato fruits to an unprecedented depth. The data demonstrate the robustness of the approach which we propose for use in upcoming large-scale projects that aim to map crop tissue proteomes.

Cathepsin D Drives the Formation of Hybrid Insulin Peptides Relevant to the Pathogenesis of Type 1 Diabetes

Hybrid insulin peptides (HIPs) form in pancreatic β-cells through the formation of peptide bonds between proinsulin fragments and other peptides. HIPs have been identified in pancreatic islets by mass spectrometry and are targeted by CD4 T cells in patients with type 1 diabetes (T1D) as well as by pathogenic CD4 T-cell clones in nonobese diabetic (NOD) mice. The mechanism of HIP formation is currently poorly understood; however, it is well established that proteases can drive the formation of new peptide bonds in a side reaction during peptide bond hydrolysis. Here, we used a proteomic strategy on enriched insulin granules and identified cathepsin D (CatD) as the primary protease driving the specific formation of HIPs targeted by disease-relevant CD4 T cells in T1D. We also established that NOD islets deficient in cathepsin L (CatL), another protease implicated in the formation of disease-relevant HIPs, contain elevated levels of HIPs, indicating a role for CatL in the proteolytic degradation of HIPs. In summary, our data suggest that CatD may be a therapeutic target in efforts to prevent or slow the autoimmune destruction of β-cells mediated by HIP-reactive CD4 T cells in T1D.

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.

Alpha-Tri: a deep neural network for scoring the similarity between predicted and measured spectra improves peptide identification of DIA data

MOTIVATION

Peptide identification of data-independent acquisition (DIA) mass spectrometry applying the peptide-centric approach heavily relies on the spectral library matching, such as the fragment intensity similarity. If the intensity similarity is calculated through all possible fragment ions of a targeted peptide instead of just a few fragment ions provided by the spectral library, the matching will be more comprehensive and reliable, and thus the identification will be more confident. In addition, the emergence of high precision spectrum predictors, like Prosit, also makes it possible to capitalize on the predicted spectrum, which contains all possible fragment ion intensities, to calculate the intensity similarity for DIA data.

RESULTS

In this work, we propose Alpha-Tri, a neural-network-based model to calculate intensity similarity as a post-processing score using the predicted spectrum, measured spectrum and correlation spectrum (triple-spectrum). The predicted spectrum is generated by Prosit, the measured spectrum is retrieved from the apex of the chromatograms of all possible fragment ions and the correlation spectrum is used to indicate the present probabilities of these fragment ions as the link between the precursor and its fragment ions is lost in DIA. By adopting a data-driven method, Alpha-Tri is able to learn the intensity similarity from the triple-spectrum. This learned value is appended to initial scores from DIA-NN, allowing the ensuing statistical validation tool to report more peptides at the same false discovery rate (FDR). In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5 to 2 h, Alpha-Tri delivered 3.0-7.2% gains in peptide detections at 1% FDR. On LFQbench dataset, a mixed-species dataset with known ratios, Alpha-Tri identified more peptides and proteins fell within the valid ratio ranges by up to 8.6% and 7.6%, respectively, compared with DIA-NN solely.

AVAILABILITY AND IMPLEMENTATION

The original datasets for benchmarks are downloaded from the ProteomeXchange with the identifiers PXD005573, PXD000954 and PXD002952. Source code is available at https://github.com/YuAirLab/Alpha-Tri.

Spotlight on alternative frame coding: Two long overlapping genes in Pseudomonas aeruginosa are translated and under purifying selection

The existence of overlapping genes (OLGs) with significant coding overlaps revolutionizes our understanding of genomic complexity. We report two exceptionally long (957 nt and 1536 nt), evolutionarily novel, translated antisense open reading frames (ORFs) embedded within annotated genes in the pathogenic Gram-negative bacterium Pseudomonas aeruginosa. Both OLG pairs show sequence features consistent with being genes and transcriptional signals in RNA sequencing. Translation of both OLGs was confirmed by ribosome profiling and mass spectrometry. Quantitative proteomics of samples taken during different phases of growth revealed regulation of protein abundances, implying biological functionality. Both OLGs are taxonomically restricted, and likely arose by overprinting within the genus. Evidence for purifying selection further supports functionality. The OLGs reported here, designated olg1 and olg2, are the longest yet proposed in prokaryotes and are among the best attested in terms of translation and evolutionary constraint. These results highlight a potentially large unexplored dimension of prokaryotic genomes.

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Two novel, very long, overlapping genes were found in Pseudomonas aeruginosa

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Both overlapping genes, olg1 and olg2, are transcribed, translated, and regulated

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Mass spectrometry verifies translation of the overlapping and their mother genes

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Both overlapping genes are taxonomically restricted, but under purifying selection

Deep learning approaches for data-independent acquisition proteomics

INTRODUCTION

Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

AREAS COVERED

This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases.

EXPERT OPINION

While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

An Introduction to Advanced Targeted Acquisition Methods

Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field.

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Advanced acquisition methods improve focus of mass spectrometers on target peptides.

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This review discusses existing methods based on two strategies.

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Retention time adjustment-based methods enable intelligent scheduling of peptide RTs.

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In spike-in triggered acquisition methods targeted scans are initiated by spike-ins.

A Novel Spectral Annotation Strategy Streamlines Reporting of mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ

Mass-spectrometry-enabled ADP-ribosylation workflows are developing rapidly, providing researchers a variety of ADP-ribosylome enrichment strategies and mass spectrometric acquisition options. Despite the growth spurt in upstream technologies, systematic ADP-ribosyl (ADPr) peptide mass spectral annotation methods are lacking. HCD-dependent ADP-ribosylome studies are common, but the resulting MS2 spectra are complex, owing to a mixture of b/y-ions and the m/p-ion peaks representing one or more dissociation events of the ADPr moiety (m-ion) and peptide (p-ion). In particular, p-ions that dissociate further into one or more fragment ions can dominate HCD spectra but are not recognized by standard spectral annotation workflows. As a result, annotation strategies that are solely reliant upon the b/y-ions result in lower spectral scores that in turn reduce the number of reportable ADPr peptides. To improve the confidence of spectral assignments, we implemented an ADPr peptide annotation and scoring strategy. All MS2 spectra are scored for the ADPr m-ions, but once spectra are assigned as an ADPr peptide, they are further annotated and scored for the p-ions. We implemented this novel workflow to ADPr peptides enriched from the liver and spleen isolated from mice post 4 h exposure to systemic IFN-γ. HCD collision energy experiments were first performed on the Orbitrap Fusion Lumos and the Q Exactive, with notable ADPr peptide dissociation properties verified with CID (Lumos). The m-ion and p-ion series score distributions revealed that ADPr peptide dissociation properties vary markedly between instruments and within instrument collision energy settings, with consequences on ADPr peptide reporting and amino acid localization. Consequentially, we increased the number of reportable ADPr peptides by 25% (liver) and 17% (spleen) by validation and the inclusion of lower confidence ADPr peptide spectra. This systematic annotation strategy will streamline future reporting of ADPr peptides that have been sequenced using any HCD/CID-based method.

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An annotation method to identify and score ADP-ribosyl (ADPr) peptide MS2 spectra.

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The m-ion score monitors the dissociation of the ADPr modification.

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The p-ion score monitors the dissociation of the peptide plus residual ADPr fragment.

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The p-ion score increased reportable ADPr peptide numbers in mouse tissues.

Construction of à la carte QconCAT protein standards for multiplexed quantification of user-specified target proteins

Background

QconCATs are quantitative concatamers for proteomic applications that yield stoichiometric quantities of sets of stable isotope-labelled internal standards. However, changing a QconCAT design, for example, to replace poorly performing peptide standards has been a protracted process.

Results

We report a new approach to the assembly and construction of QconCATs, based on synthetic biology precepts of biobricks, making use of loop assembly to construct larger entities from individual biobricks. The basic building block (a Qbrick) is a segment of DNA that encodes two or more quantification peptides for a single protein, readily held in a repository as a library resource. These Qbricks are then assembled in a one tube ligation reaction that enforces the order of assembly, to yield short QconCATs that are useable for small quantification products. However, the DNA context of the short construct also allows a second cycle of loop assembly such that five different short QconCATs can be assembled into a longer QconCAT in a second, single tube ligation. From a library of Qbricks, a bespoke QconCAT can be assembled quickly and efficiently in a form suitable for expression and labelling in vivo or in vitro.

Conclusions

We refer to this approach as the ALACAT strategy as it permits à la carte design of quantification standards. ALACAT methodology is a major gain in flexibility of QconCAT implementation as it supports rapid editing and improvement of QconCATs and permits, for example, substitution of one peptide by another.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01135-9.

[Research progress and application of retention time prediction method based on deep learning]

In "shotgun" proteomics strategy, the proteome is explained by analyzing tryptic digested peptides using liquid chromatography-mass spectrometry. In this strategy, the retention time of peptides in liquid chromatography separation can be predicted based on the peptide sequence. This is a useful feature for peptide identification. Therefore, the prediction of the retention time has attracted much research attention. Traditional methods calculate the physical and chemical properties of the peptides based on their amino acid sequence to obtain the retention time under certain chromatography conditions; however, these methods cannot be directly adopted for other chromatography conditions, nor can they be used across laboratories or instrument platforms. To solve this problem, in recent years, deep learning was introduced to proteomics research for retention time prediction. Deep learning is an advanced machine-learning method that has extraordinary capability to learn complex relationships from large-scale data. By stacking multiple hidden neural networks, deep learning can ingest raw data without manually designed features. Transfer learning is an important method in deep learning. It improves the learning process a new task through the transfer of knowledge from an already-learned related task. Transfer learning allows models trained using large datasets to be utilized across conditions by fine-tuning on smaller datasets, instead of retraining the whole model. Many retention time prediction methods have been developed. In the process of training the model, the sequences of peptides are encoded to represent peptide information. Deep learning considers the relationship between the characteristics of the peptides and their corresponding retention times without the need for manual input of the physical and chemical properties of the peptides. Compared with traditional methods, deep learning methods have higher accuracy and can be easily used under different chromatography conditions by transfer learning. If there are not enough datasets to train a new model, a trained model from other datasets can be used as a replacement after calibration with small datasets obtained from these chromatography conditions. While the retention times of modified peptides can also be predicted, the predictions are inadequate for complex modifications such as glycosylation, and this is one of the main problems to be solved. The predicted retention times were used to control the quality of peptide identification. With high accuracy, the predicted retention times can be considered as actual retention times. Therefore, the difference between predicted and observed retention times can serve as an effective and unbiased quantitative metric for evaluating the quality of peptide-spectrum matches (PSMs) reported using different peptide identification methods. Combined with fragment ion intensity prediction, retention time prediction is used to generate spectral libraries for data-independent acquisition (DIA)-based mass spectrometry analysis. Generally, DIA methods identify peptides using specific spectrum libraries obtained from data-dependent acquisition (DDA) experiments. As a result, only peptides detected in the DDA experiments can be present in the libraries and detected in DIA. Furthermore, it takes a lot of time and effort to build libraries from DDA experiments, and typically, they cannot be adopted across different laboratories or instrument platforms. In contrast, the pseudo spectral libraries generated by retention times and fragment ion intensity prediction can overcome these shortcomings. The pseudo spectral libraries generate theoretical spectra of all possible peptides without the need for DDA experiments. This paper reviews the research progress of deep learning methods in the prediction of retention time and in related applications in order to provide references for retention time prediction and protein identification. At the same time, the development direction and application trend of retention time prediction methods based on deep learning are discussed.

Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into eight groups (four charges for both tryptic/nontryptic peptides). Position-dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide's ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in a preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position-dependent ISP behavior consistent with electrostatic interaction with the peptide macrodipole, which affects the peptide helicity. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository (http://jpostdb.org) with the dataset identifiers PXD021440/JPST000959, PXD022800/JPST001017, and PXD026087/ JPST001176.

Proteome Profiling of Recombinant DNase Therapy in Reducing NETs and Aiding Recovery in COVID-19 Patients

Severe coronavirus disease 2019 (COVID-19) can result in pneumonia and acute respiratory failure. Accumulation of mucus in the airways is a hallmark of the disease and can result in hypoxemia. Here, we show that quantitative proteome analysis of the sputum from severe patients with COVID-19 reveal high levels of neutrophil extracellular trap (NET) components, which was confirmed by microscopy. Extracellular DNA from excessive NET formation can increase sputum viscosity and lead to acute respiratory distress syndrome. Recombinant human DNase (Pulmozyme; Roche) has been shown to be beneficial in reducing sputum viscosity and improve lung function. We treated five patients pwith COVID-19 resenting acute symptoms with clinically approved aerosolized Pulmozyme. No adverse reactions to the drug were seen, and improved oxygen saturation and recovery in all severely ill patients with COVID-19 was observed after therapy. Immunofluorescence and proteome analysis of sputum and blood plasma samples after treatment revealed a marked reduction of NETs and a set of statistically significant proteome changes that indicate reduction of hemorrhage, plasma leakage and inflammation in the airways, and reduced systemic inflammatory state in the blood plasma of patients. Taken together, the results indicate that NETs contribute to acute respiratory failure in COVID-19 and that degrading NETs may reduce dependency on external high-flow oxygen therapy in patients. Targeting NETs using recombinant human DNase may have significant therapeutic implications in COVID-19 disease and warrants further studies.

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High levels of neutrophil extracellular traps (NETs) in the sputum of severe COVID-19 patients.

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Recombinant human DNase decreased NETs in sputum.

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Reduced NETs were associated with recovery and improved oxygenation.

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Mass spectrometry analyses of plasma and sputum indicate resolution of inflammation.

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.

Characterization of Human CD4 T Cells Specific for a C-Peptide/C-Peptide Hybrid Insulin Peptide

Hybrid Insulin Peptides (HIPs), which consist of insulin fragments fused to other peptides from β-cell secretory granule proteins, are CD4 T cell autoantigens in type 1 diabetes (T1D). We have studied HIPs and HIP-reactive CD4 T cells extensively in the context of the non-obese diabetic (NOD) mouse model of autoimmune diabetes and have shown that CD4 T cells specific for HIPs are major contributors to disease pathogenesis. Additionally, in the human context, HIP-reactive CD4 T cells can be found in the islets and peripheral blood of T1D patients. Here, we performed an in-depth characterization of the CD4 T cell response to a C-peptide/C-peptide HIP (HIP11) in human T1D. We identified the TCR expressed by the previously-reported HIP11-reactive CD4 T cell clone E2, which was isolated from the peripheral blood of a T1D patient, and determined that it recognizes HIP11 in the context of HLA-DQ2. We also identified a HIP11-specific TCR directly in the islets of a T1D donor and demonstrated that this TCR recognizes a different minimal epitope of HIP11 presented by HLA-DQ8. We generated and tested an HLA-DQ2 tetramer loaded with HIP11 that will enable direct ex vivo interrogation of CD4 T cell responses to HIP11 in human patients and control subjects. Using mass spectrometric analysis, we confirmed that HIP11 is present in human islets. This work represents an important step in characterizing the role of CD4 T cell responses to HIPs in human T1D.

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

Peptide-Spectrum Match Validation with Internal Standards (P-VIS): Internally-Controlled Validation of Mass Spectrometry-Based Peptide Identifications

Liquid chromatography-tandem mass spectrometry is an increasingly powerful tool for studying proteins in the context of disease. As technological advances in instrumentation and data analysis have enabled deeper profiling of proteomes and peptidomes, the need for a rigorous, standardized approach to validate individual peptide-spectrum matches (PSMs) has emerged. To address this need, we developed a novel and broadly applicable workflow: PSM validation with internal standards (P-VIS). In this approach, the fragmentation spectrum and chromatographic retention time of a peptide within a biological sample are compared with those of a synthetic version of the putative peptide sequence match. Similarity measurements obtained for a panel of internal standard peptides are then used to calculate a prediction interval for valid matches. If the observed degree of similarity between the biological and the synthetic peptide falls within this prediction interval, then the match is considered valid. P-VIS enables systematic and objective assessment of the validity of individual PSMs, providing a measurable degree of confidence when identifying peptides by mass spectrometry.

Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing

As the COVID-19 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome on viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC-MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing.

ProteomicsDB: a multi-omics and multi-organism resource for life science research

ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.

Generating high quality libraries for DIA MS with empirically corrected peptide predictions

Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.

Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS

Nano-flow liquid chromatography tandem mass spectrometry (nano-flow LC–MS/MS) is the mainstay in proteome research because of its excellent sensitivity but often comes at the expense of robustness. Here we show that micro-flow LC–MS/MS using a 1 × 150 mm column shows excellent reproducibility of chromatographic retention time (<0.3% coefficient of variation, CV) and protein quantification (<7.5% CV) using data from >2000 samples of human cell lines, tissues and body fluids. Deep proteome analysis identifies >9000 proteins and >120,000 peptides in 16 h and sample multiplexing using tandem mass tags increases throughput to 11 proteomes in 16 h. The system identifies >30,000 phosphopeptides in 12 h and protein-protein or protein-drug interaction experiments can be analyzed in 20 min per sample. We show that the same column can be used to analyze >7500 samples without apparent loss of performance. This study demonstrates that micro-flow LC–MS/MS is suitable for a broad range of proteomic applications.

Mass spectrometry-based proteomics typically relies on highly sensitive nano-flow liquid chromatography (LC) but this can reduce robustness and reproducibility. Here, the authors show that micro-flow LC enables robust and reproducible high-throughput proteomics experiments at a very moderate loss of sensitivity.

ProteomicsDB

ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC–MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein–protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

Rebuilding core abscisic acid signaling pathways of Arabidopsis in yeast

The phytohormone abscisic acid (ABA) regulates plant responses to abiotic stress, such as drought and high osmotic conditions. The multitude of functionally redundant components involved in ABA signaling poses a major challenge for elucidating individual contributions to the response selectivity and sensitivity of the pathway. Here, we reconstructed single ABA signaling pathways in yeast for combinatorial analysis of ABA receptors and coreceptors, downstream‐acting SnRK2 protein kinases, and transcription factors. The analysis shows that some ABA receptors stimulate the pathway even in the absence of ABA and that SnRK2s are major determinants of ABA responsiveness by differing in the ligand‐dependent control. Five SnRK2s, including SnRK2.4 known to be active under osmotic stress in plants, activated ABA‐responsive transcription factors and were regulated by ABA receptor complexes in yeast. In the plant tissue, SnRK2.4 and ABA receptors competed for coreceptor interaction in an ABA‐dependent manner consistent with a tight integration of SnRK2.4 into the ABA signaling pathway. The study establishes the suitability of the yeast system for the dissection of core signaling cascades and opens up future avenues of research on ligand‐receptor regulation.

Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry

INTRODUCTION

Numerous diseases are caused by changes in post-translational modifications (PTMs). Therefore, the number of clinical proteomics studies that include the analysis of PTMs is increasing. Combining complementary information-for example changes in protein abundance, PTM levels, with the genome and transcriptome (proteogenomics)-holds great promise for discovering important drivers and markers of disease, as variations in copy number, expression levels, or mutations without spatial/functional/isoform information is often insufficient or even misleading. Areas covered: We discuss general considerations, requirements, pitfalls, and future perspectives in applying PTM-centric proteomics to clinical samples. This includes samples obtained from a human subject, for instance (i) bodily fluids such as plasma, urine, or cerebrospinal fluid, (ii) primary cells such as reproductive cells, blood cells, and (iii) tissue samples/biopsies. Expert commentary: PTM-centric discovery proteomics can substantially contribute to the understanding of disease mechanisms by identifying signatures with potential diagnostic or even therapeutic relevance but may require coordinated efforts of interdisciplinary and eventually multi-national consortia, such as initiated in the cancer moonshot program. Additionally, robust and standardized mass spectrometry (MS) assays-particularly targeted MS, MALDI imaging, and immuno-MALDI-may be transferred to the clinic to improve patient stratification for precision medicine, and guide therapies.

Identification of Hybrid Insulin Peptides (HIPs) in Mouse and Human Islets by Mass Spectrometry

We recently discovered hybrid insulin peptides (HIPs) as a novel class of post-translationally modified peptides in murine-derived beta cell tumors, and we demonstrated that these molecules are autoantigens in type 1 diabetes (T1D). A HIP consists of an insulin fragment linked to another secretory granule peptide via a peptide bond. We verified that autoreactive CD4 T cells in both mouse and human autoimmune diabetes recognize these modified peptides. Here, we use mass spectrometric analyses to confirm the presence of HIPs in both mouse and human pancreatic islets. We also present criteria for the confident identification of these peptides. This work supports the hypothesis that HIPs are autoantigens in human T1D and provides a foundation for future efforts to interrogate this previously unknown component of the beta cell proteome.

Universal retention standard for peptide separations using various modes of high-performance liquid chromatography

Peptide retention standards are widely used by chromatography specialists. They can be used for quality control of peptide separations (separation efficiency, selectivity, retention values) and for accurate concatenation of retention data from multiple acquisitions in proteomics. So far the repertoire of available retention standards is mostly limited to reversed-phase separations. We introduce a synthetic peptide mixture which can be used in conjunction with the most popular peptide separation techniques: reversed-phase (RPLC), strong-cation exchange (SCX), (strong-anion exchange) SAX and hydrophilic interaction liquid chromatography (HILIC). Target sequences were first designed in-silico using Sequence-Specific Retention Calculator models covering all major peptide separation mechanisms. Peptides were also designed while keeping in mind the simplicity of retention time assignment using MS detection: they all have nearly identical masses and identical intense y₃ fragment ions. This contribution demonstrates the application of this mixture for characterization of eight HILIC as well as SAX, SCX and C18 columns.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

ProteomeTools: Systematic Characterization of 21 Post-translational Protein Modifications by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Using Synthetic Peptides

The analysis of the post-translational modification (PTM) state of proteins using mass spectrometry-based bottom-up proteomic workflows has evolved into a powerful tool for the study of cellular regulatory events that are not directly encoded at the genome level. Besides frequently detected modifications such as phosphorylation, acetylation and ubiquitination, many low abundant or less frequently detected PTMs are known or postulated to serve important regulatory functions. To more broadly understand the LC-MS/MS characteristics of PTMs, we synthesized and analyzed ∼5,000 peptides representing 21 different naturally occurring modifications of lysine, arginine, proline and tyrosine side chains and their unmodified counterparts. The analysis identified changes in retention times, shifts of precursor charge states and differences in search engine scores between modifications. PTM-dependent changes in the fragmentation behavior were evaluated using eleven different fragmentation modes or collision energies. We also systematically investigated the formation of diagnostic ions or neutral losses for all PTMs, confirming 10 known and identifying 5 novel diagnostic ions for lysine modifications. To demonstrate the value of including diagnostic ions in database searching, we reprocessed a public data set of lysine crotonylation and showed that considering the diagnostic ions increases confidence in the identification of the modified peptides. To our knowledge, this constitutes the first broad and systematic analysis of the LC-MS/MS properties of common and rare PTMs using synthetic peptides, leading to direct applicable utility for bottom-up proteomic experiments.