Protein analysis by shotgun/bottom-up proteomics

A guide to mass spectrometric analysis of extracellular vesicle proteins for biomarker discovery

Exosomes (small extracellular vesicles) in living organisms play an important role in processes such as cell proliferation or intercellular communication. Recently, exosomes have been extensively investigated for biomarker discoveries for various diseases. An important aspect of exosome analysis involves the development of enrichment methods that have been introduced for successful isolation of exosomes. These methods include ultracentrifugation, size exclusion chromatography, polyethylene glycol-based precipitation, immunoaffinity-based enrichment, ultrafiltration, and asymmetric flow field-flow fractionation among others. To confirm the presence of exosomes, various characterization methods have been utilized such as Western blot analysis, atomic force microscopy, electron microscopy, optical methods, zeta potential, visual inspection, and mass spectrometry. Recent advances in high-resolution separations, high-performance mass spectrometry and comprehensive proteome databases have all contributed to the successful analysis of exosomes from patient samples. Herein we review various exosome enrichment methods, characterization methods, and recent trends of exosome investigations using mass spectrometry-based approaches for biomarker discovery.

Automated proteomic sample preparation: The key component for high throughput and quantitative mass spectrometry analysis

Sample preparation for mass spectrometry-based proteomics has many tedious and time-consuming steps that can introduce analytical errors. In particular, the steps around the proteolytic digestion of protein samples are prone to inconsistency. One route for reliable sample processing is the development and optimization of a workflow utilizing an automated liquid handling workstation. Diligent assessment of the sample type, protocol design, reagents, and incubation conditions can significantly improve the speed and consistency of preparation. When combining robust liquid chromatography-mass spectrometry with either discovery or targeted methods, automated sample preparation facilitates increased throughput and reproducible quantitation of biomarker candidates. These improvements in analysis are also essential to process the large patient cohorts necessary to validate a candidate biomarker for potential clinical use. This article reviews the steps in the workflow, optimization strategies, and known applications in clinical, pharmaceutical, and research fields that demonstrate the broad utility for improved automation of sample preparation in the proteomic field.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

Stability of enzyme immobilized on the nanofluidic channel surface

The lifetime of an enzyme is critical to prevent system failure and optimize maintenance schedules in biological and analytical chemistry. The lifetime metrics of an enzyme can be evaluated from enzyme activity in terms of catalytic cycles per enzyme at various storage times. Trypsin, which is a gold-standard enzyme in proteomics, has been known to decrease activity due to self-digestion. To improve the activity of trypsin, enzyme reactors have developed by immobilizing in micro and nanospace. However, an evaluation method for the catalytic cycle has not been established due to major issues such as nonuniform space, unstable liquid transport, and self-digestion during immobilization in conventional work. To solve these issues, we have previously developed an ultra-fast enzyme reactor with a well-defined nanofabrication method, stable liquid transport, and partial enzyme modification. Here, we aimed to investigate catalytic cycles in a nanochannel. To extend enzyme lifetime efficiently, we have evaluated the optimal immobilization process and catalytic cycles of trypsin. As a result, immobilized enzyme densities by the trypsinogen immobilization process were increased at all concentrations compared to the trypsin immobilization process. To evaluate the lifetime of trypsin, the immobilized enzyme densities and activities were almost the same before and after 72 h of enzyme storage, and the calculated catalytic cycles were 1740. These results indicated that self-digestion of the immobilized enzyme was highly suppressed. Consequently, the reaction efficiency has been evaluated depending on the catalytic cycles from the substrate for the first time, while preventing self-digestion by trypsin.

Recent advances (2019-2021) of capillary electrophoresis-mass spectrometry for multilevel proteomics

Multilevel proteomics aims to delineate proteins at the peptide (bottom-up proteomics), proteoform (top-down proteomics), and protein complex (native proteomics) levels. Capillary electrophoresis-mass spectrometry (CE-MS) can achieve highly efficient separation and highly sensitive detection of complex mixtures of peptides, proteoforms, and even protein complexes because of its substantial technical progress. CE-MS has become a valuable alternative to the routinely used liquid chromatography-mass spectrometry for multilevel proteomics. This review summarizes the most recent (2019-2021) advances of CE-MS for multilevel proteomics regarding technological progress and biological applications. We also provide brief perspectives on CE-MS for multilevel proteomics at the end, highlighting some future directions and potential challenges.

Supramolecular framework membrane for precise sieving of small molecules, nanoparticles and proteins

Synthetic framework materials have been cherished as appealing candidates for separation membranes in daily life and industry, while the challenges still remain in precise control of aperture distribution and separation threshold, mild processing methods, and extensive application aspects. Here, we show a two-dimensional (2D) processible supramolecular framework (SF) by integrating directional organic host-guest motifs and inorganic functional polyanionic clusters. The thickness and flexibility of the obtained 2D SFs are tuned by the solvent modulation to the interlayer interactions, and the optimized SFs with limited layers but micron-sized areas are used to fabricate the sustainable membranes. The uniform nanopores allow the membrane composed of layered SF to exhibit strict size retention for substrates with the rejection value of 3.8 nm, and the separation accuracy within 5 kDa for proteins. Furthermore, the membrane performs high charge selectivity for charged organics, nanoparticles, and proteins, due to the insertion of polyanionic clusters in the framework skeletons. This work displays the extensional separation potentials of self-assembled framework membranes comprising of small-molecules and provides a platform for the preparation of multifunctional framework materials due to the conveniently ionic exchange of the counterions of the polyanionic clusters.

Atomistic Details of Peptide Reversed-Phase Liquid Chromatography from Molecular Dynamics Simulations

Peptide separations by reversed-phase liquid chromatography (RPLC) are an integral part of bottom-up proteomics. These separations typically employ C18 columns with water/acetonitrile gradient elution in the presence of formic acid. Despite the widespread use of such workflows, the exact nature of peptide interactions with the stationary and mobile phases is poorly understood. Here, we employ microsecond molecular dynamics (MD) simulations to uncover details of peptide RPLC. We examined two tryptic peptides, a hydrophobic and a hydrophilic species, in a slit pore lined with C18 chains that were grafted onto SiO₂ support. Our simulations explored peptide trapping, followed by desorption and elution. Trapping in an aqueous mobile phase was initiated by C18 contacts with Lys butyl moieties. This was followed by extensive anchoring of nonpolar side chains (Leu/Ile/Val) in the C18 layer. Exposure to water/acetonitrile triggered peptide desorption in a stepwise fashion; charged sites close to the termini were the first to lift off, followed by the other residues. During water/acetonitrile elution, both peptides preferentially resided close to the pore center. The hydrophilic peptide exhibited no contacts with the stationary phase under these conditions. In contrast, the hydrophobic species underwent multiple transient Leu/Ile/Val binding interactions with C18 chains. These nonpolar interactions represent the foundation of differential peptide retention, in agreement with the experimental elution behavior of the two peptides. Extensive peptide/formate ion pairing was observed in water/acetonitrile, particularly at N-terminal sites. Overall, this work uncovers an unprecedented level of RPLC molecular details, paving the way for MD simulations as a future tool for improving retention prediction algorithms and for the design of novel column materials.

Miniprep assisted proteomics (MAP) for rapid proteomics sample preparation

Complete enzymatic digestion of proteins for bottom-up proteomics is substantially improved by use of detergents for denaturation and solubilization. Detergents however, are incompatible with many proteases and highly detrimental to LC-MS/MS. Recently; filter-based methods have seen wide use due to their capacity to remove detergents and harmful reagents prior to digestion and mass spectrometric analysis. We hypothesized that non-specific protein binding to negatively charged silica-based filters would be enhanced by addition of lyotropic salts, similar to DNA purification. We sought to exploit these interactions and investigate if low-cost DNA purification spin-filters, 'Minipreps,' efficiently and reproducibly bind proteins for digestion and LC-MS/MS analysis. We propose a new method, Miniprep Assisted Proteomics (MAP), for sample preparation. We demonstrate binding capacity, performance, recovery and identification rates for proteins and whole-cell lysates using MAP. MAP recovered equivalent or greater protein yields from 0.5-50 μg analyses benchmarked against commercial trapping preparations. Nano UHPLC-MS/MS proteome profiling of lysates of Escherichia coli had 99.3% overlap vs. existing approaches and reproducibility of replicate minipreps was 98.8% at the 1% FDR protein level. Label Free Quantitative proteomics was performed and 91.2% of quantified proteins had a %CV <20% (2044/2241). Miniprep Assisted Proteomics can be performed in minutes, shows low variability, high recovery and proteome depth. This suggests a significant role for adventitious binding in developing new proteomics sample preparation techniques. MAP represents an efficient, ultra-low-cost alternative for sample preparation in a commercially obtainable device that costs ∼$0.50 (USD) per miniprep.

Patpat: a public proteomics dataset search framework

SUMMARY

As the FAIR (Findable, Accessible, Interoperable, Reusable) principles have become widely accepted in the proteomics field, under the guidance of ProteomeXchange and The Human Proteome Organization Proteomics Standards Initiative, proteomics public databases have been providing Application Programming Interfaces for programmatic access. Based on generating logic from proteomics data, we present Patpat, an extensible framework for searching public datasets, merging results from multiple databases to help researchers find their proteins of interest in the vast mass spectrometry. Patpat's 2D strategy of combining results from multiple databases allows users to provide only protein identifiers to obtain metadata for relevant datasets, improving the 'Findable' of proteomics data.

AVAILABILITY AND IMPLEMENTATION

The Patpat framework is released under the Apache 2.0 license open source, and the source code is stored on GitHub (https://github.com/henry-leo/Patpat) and is freely available.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sequence Coverage Visualizer: A Web Application for Protein Sequence Coverage 3D Visualization

Protein structure defines protein function and plays an extremely important role in protein characterization. Recently, two groups of researchers from DeepMind and the Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. This enabled us to visualize the entire human proteome using predicted 3D structures for the first time. To help other researchers best utilize these protein structure predictions in proteomics experiments, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular proteomics experiment (identified peptide list) into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help to compare different protein structures from different sources, including predicted ones and existing PDB entries. We hope our tool can provide help in the process of improving protein structure prediction accuracy. Overall, SCV is a convenient and powerful tool for visualizing proteomics results in 3D.

[Antibody proteomics]

Antibodies represent an essential component of humoral immunity; therefore their study is important for molecular biology and medicine. The unique property of antibodies to specifically recognize and bind a certain molecular target (an antigen) determines their widespread application in treatment and diagnostics of diseases, as well as in laboratory and biotechnological practices. High specificity and affinity of antibodies is determined by the presence of primary structure variable regions, which are not encoded in the human genome and are unique for each antibody-producing B cell clone. Hence, there is little or no information about amino acid sequences of the variable regions in the databases. This differs identification of antibody primary structure from most of the proteomic studies because it requires either B cell genome sequencing or de novo amino acid sequencing of the antibody. The present review demonstrates some examples of proteomic and proteogenomic approaches and the methodological arsenal that proteomics can offer for studying antibodies, in particular, for identification of primary structure, evaluation of posttranslational modifications and application of bioinformatics tools for their decoding.

Non-enzymatic acetylation inhibits glycolytic enzymes in Escherichia coli

Advanced mass spectrometry methods have detected thousands of post-translational phosphorylation and acetylation sites in bacteria, but their functional role and the enzymes catalyzing these modifications remain largely unknown. In addition to enzymatic acetylation, lysine residues can also be chemically acetylated by the metabolite acetyl phosphate. In Escherichia coli, acetylation at over 3,000 sites has been linked to acetyl phosphate, but the functionality of this widespread non-enzymatic acetylation is even less clear than the enzyme-catalyzed one. Here, we investigate the role of acetyl-phosphate-mediated acetylation in E. coli central metabolism. Out of 19 enzymes investigated, only GapA and GpmA are acetylated at high stoichiometry, which inhibits their activity by interfering with substrate binding, effectively reducing glycolysis when flux to or from acetate is high. Extrapolating our results to the whole proteome, maximally 10% of the reported non-enzymatically acetylated proteins are expected to reach a stoichiometry that could inhibit their activity.

Overview and considerations in bottom-up proteomics

Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.

Fiber-Type Shifting in Sarcopenia of Old Age: Proteomic Profiling of the Contractile Apparatus of Skeletal Muscles

The progressive loss of skeletal muscle mass and concomitant reduction in contractile strength plays a central role in frailty syndrome. Age-related neuronal impairments are closely associated with sarcopenia in the elderly, which is characterized by severe muscular atrophy that can considerably lessen the overall quality of life at old age. Mass-spectrometry-based proteomic surveys of senescent human skeletal muscles, as well as animal models of sarcopenia, have decisively improved our understanding of the molecular and cellular consequences of muscular atrophy and associated fiber-type shifting during aging. This review outlines the mass spectrometric identification of proteome-wide changes in atrophying skeletal muscles, with a focus on contractile proteins as potential markers of changes in fiber-type distribution patterns. The observed trend of fast-to-slow transitions in individual human skeletal muscles during the aging process is most likely linked to a preferential susceptibility of fast-twitching muscle fibers to muscular atrophy. Studies with senescent animal models, including mostly aged rodent skeletal muscles, have confirmed fiber-type shifting. The proteomic analysis of fast versus slow isoforms of key contractile proteins, such as myosin heavy chains, myosin light chains, actins, troponins and tropomyosins, suggests them as suitable bioanalytical tools of fiber-type transitions during aging.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

Contributions from ClpS surface residues in modulating N-terminal peptide binding and their implications for NAAB development

Numerous technologies are currently in development for use in next-generation protein sequencing platforms. A notable published approach employs fluorescently-tagged binding proteins to identity the N-terminus of immobilized peptides, in-between rounds of digestion. This approach makes use of N-terminal amino acid binder (NAAB) proteins, which would identify amino acids by chemical and shape complementarity. One source of NAABs is the ClpS protein family, which serve to recruit proteins to bacterial proteosomes based on the identity of the N-terminal amino acid. In this study, a Thermosynechococcus vestitus (also known as Thermosynechococcus elongatus) ClpS2 protein was used as the starting point for direct evolution of an NAAB with affinity and specificity for N-terminal leucine. Enriched variants were analyzed and shown to improve the interaction between the ClpS surface and the peptide chain, without increasing promiscuity. Interestingly, interactions were found that were unanticipated which favor different charged residues located at position 5 from the N-terminus of a target peptide.

Label-Free Quantification from Direct Infusion Shotgun Proteome Analysis (DISPA-LFQ) with CsoDIAq Software

Large-scale proteome analysis requires rapid and high-throughput analytical methods. We recently reported a new paradigm in proteome analysis where direct infusion and ion mobility are used instead of liquid chromatography (LC) to achieve rapid and high-throughput proteome analysis. Here, we introduce an improved direct infusion shotgun proteome analysis protocol including label-free quantification (DISPA-LFQ) using CsoDIAq software. With CsoDIAq analysis of DISPA data, we can now identify up to ∼2000 proteins from the HeLa and 293T proteomes, and with DISPA-LFQ, we can quantify ∼1000 proteins from no more than 1 μg of sample within minutes. The identified proteins are involved in numerous valuable pathways including central carbon metabolism, nucleic acid replication and transport, protein synthesis, and endocytosis. Together with a high-throughput sample preparation method in a 96-well plate, we further demonstrate the utility of this technology for performing high-throughput drug analysis in human 293T cells. The total time for data collection from a whole 96-well plate is approximately 8 h. We conclude that the DISPA-LFQ strategy presents a valuable tool for fast identification and quantification of proteins in complex mixtures, which will power a high-throughput proteomic era of drug screening, biomarker discovery, and clinical analysis.

Acetic Acid Ion Pairing Additive for Reversed-Phase HPLC Improves Detection Sensitivity in Bottom-up Proteomics Compared to Formic Acid

Despite the general acceptance of formic acid as the additive of choice for peptide reversed-phase LC-MS/MS applications, some still argue that the selection of acetic acid represents a better option. To settle this debate, we investigated both the difference in MS sensitivity and chromatographic behavior of peptides between these two systems. This interlaboratory study was performed using different MS setups and C18 separation media employing both 0.1% formic and 0.5% acetic acid as ion pairing modifiers. Relative to formic acid, we find an overall ∼2.2-2.5× increase in MS signal and a slight decrease in RP LC retention (-0.7% acetonitrile on average) for acetic acid conditions. While these two features have opposing effects on peptide detectability, we find that acetic acid produces up to 60% higher peptide ID output depending on the type of sample. The drop in RPLC retention increases with peptide net charge at acidic pH. MS signal is dependent on the difference between the charge of the precursor ion and the charge of the peptide in solution, favoring species with a low pI. Lower peptide retention under acetic acid conditions demonstrates its higher hydrophilicity and, as expected, leads to composition and sequence-dependent character of the observed retention shift.

A sDOE (Simple Design-of-Experiment) Approach for Parameter Optimization in Mass Spectrometry. Part 1. Parameter Selection and Interference Effects in Top-Down ETD Fragmentation of Proteins in a UHR-QTOF Instrument

Design-of-experiment (DOE) approaches, originally conceived by Fischer, are widely applied in industry, particularly in the context of production for which they have been greatly expended. In a research and development context, DOE can be of great use for method development. Specifically, DOE can greatly speed up instrument parameter optimization by first identifying parameters that are critical to a given outcome, showing parameter interdependency where it occurs and accelerating optimization of said parameters using matrices of experimental conditions. While DOE approaches have been applied in mass spectrometry experiments, they have so far failed to gain widespread adoption. This could be attributed to the fact that DOE can get quite complex and daunting to the everyday user. Here we make the case that a subset of DOE tools, hereafter called SimpleDOE (sDOE), can make DOE accessible and useful to the Mass Spectrometry community at large. We illustrate the progressive gains from a purely manual approach to sDOE through a stepwise optimization of parameters affecting the efficiency of top-down ETD fragmentation of proteins on a high-resolution Q-TOF mass spectrometer, where the aim is to maximize sequence coverage of fragmentation events.

Quantitative structural proteomics in living cells by covalent protein painting

The fold and conformation of proteins are key to successful cellular function, but all techniques for protein structure determination are performed in an artificial environment with highly purified proteins. While protein conformations have been solved to atomic resolution and modern protein structure prediction tools rapidly generate near accurate models of proteins, there is an unmet need to uncover the conformations of proteins in living cells. Here, we describe Covalent Protein Painting (CPP), a simple and fast method to infer structural information on protein conformation in cells with a quantitative protein footprinting technology. CPP monitors the conformational landscape of the 3D proteome in cells with high sensitivity and throughput. A key advantage of CPP is its' ability to quantitatively compare the 3D proteomes between different experimental conditions and to discover significant changes in the protein conformations. We detail how to perform a successful CPP experiment, the factors to consider before performing the experiment, and how to interpret the results.

Neutral-Coating Capillary Electrophoresis Coupled with High-Resolution Mass Spectrometry for Top-Down Identification of Hemoglobin Variants

BACKGROUND

Identification of hemoglobin (Hb) variants is of significant value in the clinical diagnosis of hemoglobinopathy. However, conventional methods for identification of Hb variants in clinical laboratories can be inadequate due to the lack of structural characterization. We describe the use of neutral-coating capillary electrophoresis coupled with high-resolution mass spectrometry (CE-HR-MS) to achieve high-performance top-down identification of Hb variants.

METHODS

An Orbitrap Q-Exactive Plus mass spectrometer was coupled with an ECE-001 capillary electrophoresis (CE) unit through an EMASS-II ion source. A PS1 neutral-coating capillary was used for CE. Samples of red blood cells were lysed in water and diluted in 10 mM ammonium formate buffer for analysis. Deconvolution of raw mass spectrometry data was carried out to merge multiple charge states and isotopic peaks of an analyte to obtain its monoisotopic mass.

RESULTS

The neutral-coating CE could baseline separate individual Hb subunits dissociated from intact Hb forms, and the HR-MS could achieve both intact-protein analysis and top-down analysis of analytes. A number of patient samples that contain Hb subunit variants were analyzed, and the variants were successfully identified using the CE-HR-MS method.

CONCLUSIONS

The CE-HR-MS method has been demonstrated as a useful tool for top-down identification of Hb variants. With the ability to characterize the primary structures of Hb subunits, the CE-HR-MS method has significant advantages to complement or partially replace the conventional methods for the identification of Hb variants.

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.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar

Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.

Allergy, asthma, and proteomics: opportunities with immediate impact

Allergy is widely discussed by researchers due to its complex mechanism that leads to disorders and injuries, but the reason behind the allergic status remains unclear. Current treatments are insufficient to improve the patient's quality of life significantly. New technologies in scientific and technological development are emerging. For instance, the union between allergy and peptidomics and bioinformatics tools may help fill the gaps in this field, diagnosis, and treatment. In this review, we look at peptidomics and address some findings, such as target proteins or biomarkers that help better understand mechanisms that lead to inflammation, organ damage, and, consequently, poor quality of life or even death.

In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry

Blood in the circulatory system carries information of physiological and pathological status of the human body, so blood proteins are often used as biomarkers for diagnosis, prognosis, and therapy. Human blood proteome can be explored by the latest technologies in mass spectrometry (MS), creating an opportunity of discovering new disease biomarkers. The extreme dynamic range of protein concentrations in blood, however, poses a challenge to detect proteins of low abundance, namely, tissue leakage proteins. Here, we describe a strategy to directly analyze undepleted blood samples by extensive liquid chromatography (LC) fractionation and 18-plex tandem-mass-tag (TMT) mass spectrometry. The proteins in blood specimens (e.g., plasma or serum) are isolated by acetone precipitation and digested into peptides. The resulting peptides are TMT-labeled, separated by basic pH reverse-phase (RP) LC into at least 40 fractions, and analyzed by acidic pH RPLC and high-resolution MS/MS, leading to the quantification of ~3000 unique proteins. Further increase of basic pH RPLC fractions and adjustment of the fraction concatenation strategy can enhance the proteomic coverage (up to ~5000 proteins). Finally, the combination of multiple batches of TMT experiments allows the profiling of hundreds of blood samples. This TMT-MS-based method provides a powerful platform for deep proteome profiling of human blood samples.

Proteomic and lipidomic biomarkers in the diagnosis and progression of inflammatory bowel disease - a review

PURPOSE

There is an increasing prevalence of inflammatory bowel disease (IBD) and to date, no effective treatment has been developed and the exact etiology of this disease remains unknown. Nevertheless, a growing number of proteomic and lipidomic studies have identified certain proteins and lipids which can be used successfully in patients to improve diagnoses and monitoring of treatment.

EXPERIMENTAL DESIGN

We have focused on the applications of proteins and lipids for IBD diagnostics, including differentiation of Crohn's disease (CD) and ulcerative colitis (UC), treatment monitoring, monitoring of clinical state, likelihood of relapse, and their potential for novel targeted treatments.

RESULTS

Analysis of protein and lipid profiles can: improve the availability and use of diagnostic markers; improve understanding of the pathomechanisms of IBD, for example, several studies have implicated platelet dysfunction (PF4), autoimmune responses (granzyme B, perforin), and abnormal metabolism (arachidonic acid pathways); aid in monitoring patient health; and improve therapeutics (experimental phosphatidylcholine therapy has been shown to result in an improvement in intestinal condition).

CONCLUSIONS

Despite the enormous progress of proteomics and lipidomics in recent years and the development of new technologies, further research is needed to select some of the most sensitive and specific markers applicable in diagnosing and treating IBD.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Engineering of Pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model

Pseudomonas putida KT2440 is an attractive bacterial host for biotechnological production of valuable chemicals from renewable lignocellulosic feedstocks as it can valorize lignin-derived aromatics or glucose obtainable from cellulose. P. putida EM42, a genome-reduced variant of strain KT2440 endowed with advantageous physiological properties, was recently engineered for growth on cellobiose, a major cellooligosaccharide product of enzymatic cellulose hydrolysis. Co-utilization of cellobiose and glucose was achieved in a mutant lacking periplasmic glucose dehydrogenase Gcd (PP_1444). However, the cause of the co-utilization phenotype remained to be understood and the Δgcd strain had a significant growth defect. In this study, we investigated the basis of the simultaneous uptake of the two sugars and accelerated the growth of P. putida EM42 Δgcd mutant for the bioproduction of valuable compounds from glucose and cellobiose. We show that the gcd deletion lifted the inhibition of the exogenous β-glucosidase BglC from Thermobifida fusca exerted by the intermediates of the periplasmic glucose oxidation pathway. The additional deletion of hexR gene, which encodes a repressor of the upper glycolysis genes, failed to restore rapid growth on glucose. The reduced growth rate of the Δgcd mutant was partially compensated by the implantation of heterologous glucose and cellobiose transporters (Glf from Zymomonas mobilis and LacY from Escherichia coli, respectively). Remarkably, this intervention resulted in the accumulation of pyruvate in aerobic P. putida cultures. We demonstrated that the excess of this key metabolic intermediate can be redirected to the enhanced biosynthesis of ethanol and lactate. The pyruvate overproduction phenotype was then unveiled by an upgraded genome-scale metabolic model constrained with proteomic and kinetic data. The model pointed to the saturation of glucose catabolism enzymes due to unregulated substrate uptake and it predicted improved bioproduction of pyruvate-derived chemicals by the engineered strain. This work sheds light on the co-metabolism of cellulosic sugars in an attractive biotechnological host and introduces a novel strategy for pyruvate overproduction in bacterial cultures under aerobic conditions.

Proteome Analysis of Oral Biofluids in Periodontal Health and Disease Using Mass Spectrometry

Mass spectrometry-based proteomic approaches permit the high-throughput assessment of proteins from oral biofluids, therefore, allowing a deeper insight into the mechanistic study of periodontal disease. Here we describe an entire experimental design of proteomic workflow for oral biofluids, exemplified by saliva and gingival crevicular fluid collected from periodontal health or disease subjects and using a label-free quantification strategy for mass spectrometric data acquisition.

DeGlyPHER: Highly sensitive site-specific analysis of N-linked glycans on proteins

Traditional mass spectrometry-based glycoproteomic approaches have been widely used for site-specific N-glycoform analysis, but a large amount of starting material is needed to obtain sampling that is representative of the vast diversity of N-glycans on glycoproteins. These methods also often include a complicated workflow and very challenging data analysis. These limitations have prevented glycoproteomics from being adapted to high-throughput platforms, and the sensitivity of the analysis is currently inadequate for elucidating N-glycan heterogeneity in clinical samples. Heavily glycosylated spike proteins of enveloped viruses, recombinantly expressed as potential vaccines, are prime targets for glycoproteomic analysis. Since the immunogenicity of spike proteins may be impacted by their glycosylation patterns, site-specific analysis of N-glycoforms provides critical information for vaccine design. Using recombinantly expressed soluble HIV Env trimer, we describe DeGlyPHER, a modification of our previously reported sequential deglycosylation strategy to yield a "single-pot" process. DeGlyPHER is an ultrasensitive, simple, rapid, robust, and efficient approach for site-specific analysis of protein N-glycoforms, that we developed for analysis of limited quantities of glycoproteins.

Phosphoproteomics Profile of Chicken Cecum in the Response to Salmonella enterica Serovar Enteritidis Inoculation

Salmonella enterica serovar Enteritidis (S. Enteritidis) is a foodborne pathogen, which can cause great threats to human health through the consumption of contaminated poultry products. This research combines TMT labeling, HPLC and mass-spectrometry-based phosphoproteomics on cecum of the F1 cross of Guangxi Yao chicken and Jining Bairi chicken. The treated group was inoculated with 0.3 mL inoculum S. Enteritidis, and the control group was inoculated with 0.3 mL phosphate-buffered saline (PBS). A total of 338 differentially phosphorylated modification sites in 243 differentially phosphorylated proteins (DPPs) were chosen for downstream analyses. A total of 213 sites in 146 DPPs were up-regulated and 125 sites in 97 DPPs were down-regulated. Functional analysis was performed for DPPs based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the protein domain. The DPPs were mainly enriched in immune- and metabolic-related GO-BP (biological process) and KEGG pathways. We predicted and classified the subcellular structure and COG/KOG of DPPs. Furthermore, protein-protein interaction network analyses were performed by using multiple algorithms. We identified 71 motifs of the phosphorylated modification sites and selected 18 sites randomly to detect the expression level through parallel reaction monitoring (PRM). S. Enteritidis inoculation caused phosphorylation alteration in immune- and metabolic-related proteins. The invasion of S. Enteritidis may be actualized by inducing cecum cell apoptosis through the endoplasmic reticulum pathway, and chickens could resist the invasion of S. Enteritidis by affecting the function of ECM receptors. The findings herein provide a crucial theoretical foundation to understand the molecular mechanism and epigenetic regulation in response to S. Enteritidis inoculation in chickens.

Development of Tier 2 LC-MRM-MS protein quantification methods for liquid biopsies

In the pursuit of personalized diagnostics and tailored treatments, quantitative protein tests contribute to a more precise definition of health and disease. The development of new quantitative protein tests should be driven by an unmet clinical need and performed in a collaborative effort that involves all stakeholders. With regard to the analytical part, mass spectrometry (MS)-based platforms are an excellent tool for quantification of specific proteins in body fluids, for example focused on cancer. The obtained readouts have great potential in determining tumor aggressiveness to facilitate treatment decisions, and can furthermore be used to monitor patient response. Internationally standardized TNM classifications of malignant tumors are beneficial for diagnosis, however treatment outcome and survival of cancer patients is poorly predicted. To this end, the importance of the tumor microenvironment has endorsed the introduction of the tumor-stroma ratio as a prognostic parameter in solid primary tumor types. Currently, the stromal content of tumor tissues is determined via routine diagnostic pathology slides. With the development of liquid chromatography (LC)-MS methods we aim at quantification of tumor-stroma specific proteins in body fluids. In this mini-review the analytical aspect of this developmental trajectory is further detailed.

Dual Biomimetic Recognition-Driven Plasmonic Nanogap-Enhanced Raman Scattering for Ultrasensitive Protein Fingerprinting and Quantitation

Protein assays with fingerprints and high sensitivity are essential for biomedical research and applications. However, the prevailing methods mainly rely on indirect or labeled immunoassays, failing to provide fingerprint information. Herein, we report a dual biomimetic recognition-driven plasmonic nanogap-enhanced Raman scattering (DBR-PNERS) strategy for ultrasensitive protein fingerprinting and quantitation. A pair of molecularly imprinted nanoantennas were rationally engineered for specifically trapping a target protein into well-defined plasmonic nanogaps through dual-terminal recognition for ultrahigh Raman signal amplification. Meanwhile, a Raman-active small molecule was embedded into the nanoantenna as an internal standard to provide a ratiometric assay for robust quantitation. DBR-PNERS exhibited several significant merits over existing approaches, including fingerprinting, ultrahigh sensitivity, quantitation robustness, speed, sample consumption, and so on. Therefore, it can be a promising tool for a protein assay and holds a great perspective in important applications.

Peptide mapping of proteins by capillary electromigration methods

This review article provides a wide overview of important developments and applications of capillary electromigration methods in the area of peptide mapping of proteins in the period 1997-mid-2022, including review articles on this topic. It deals with all major aspects of peptide mapping by capillary electromigration methods: i) precleavage sample preparation involving purification, preconcentration, denaturation, reduction and alkylation of protein(s) to be analyzed, ii) generation of peptide fragments by off-line or on-line enzymatic and/or chemical cleavage of protein(s), iii) postcleavage preparation of the generated peptide mixture for capillary electromigration separation, iv) separation of the complex peptide mixtures by one-, two- and multidimensional capillary electromigration methods coupled with mass spectrometry detection, and v) a large application of peptide mapping for variable purposes, such as qualitative analysis of monoclonal antibodies and other protein biopharmaceuticals, monitoring of posttranslational modifications, determination of primary structure and investigation of function of proteins in biochemical and clinical research, characterization of proteins of variable origin as well as for protein and peptide identification in proteomic and peptidomic studies.

Protein Analysis of A. halleri and N. caerulescens Hyperaccumulators When Exposed to Nano and Ionic Forms of Cd and Zn

Hyperaccumulator plant species growing on metal-rich soils can accumulate high quantity of metals and metalloids in aerial tissues, and several proteomic studies on the molecular mechanisms at the basis of metals resistance and hyperaccumulation have been published. Hyperaccumulator are also at the basis of the phytoremediation strategy to remove metals more efficiently from polluted soils or water. Arabidopsis halleri and Noccea caerulescens are both hyperaccumulators of metals and nano-metals. In this study, the change in some proteins in A. halleri and N. caerulescens was assessed after the growth in soil with cadmium and zinc, provided as sulphate salts (CdSO₄ and ZnSO₄) or sulfide quantum dots (CdS QDs and ZnS QDs). The protein extracts obtained from plants after 30 days of growth were analyzed by 2D-gel electrophoresis (2D SDS-PAGE) and identified by MALDI-TOF/TOF mass spectrometry. A bioinformatics analysis was carried out on quantitative protein differences between control and treated plants. In total, 43 proteins resulted in being significatively modulated in A. halleri, while 61 resulted in being modulated in N. caerulescens. Although these two plants are hyperaccumulator of both metals and nano-metals, at protein levels the mechanisms involved do not proceed in the same way, but at the end bring a similar physiological result.

Does filter-aided sample preparation provide sufficient method linearity for quantitative plant shotgun proteomics?

Due to its outstanding throughput and analytical resolution, gel-free LC-based shotgun proteomics represents the gold standard of proteome analysis. Thereby, the efficiency of sample preparation dramatically affects the correctness and reliability of protein quantification. Thus, the steps of protein isolation, solubilization, and proteolysis represent the principal bottleneck of shotgun proteomics. The desired performance of the sample preparation protocols can be achieved by the application of detergents. However, these compounds ultimately compromise reverse-phase chromatographic separation and disrupt electrospray ionization. Filter-aided sample preparation (FASP) represents an elegant approach to overcome these limitations. Although this method is comprehensively validated for cell proteomics, its applicability to plants and compatibility with plant-specific protein isolation protocols remain to be confirmed. Thereby, the most important gap is the absence of the data on the linearity of underlying protein quantification methods for plant matrices. To fill this gap, we address here the potential of FASP in combination with two protein isolation protocols for quantitative analysis of pea (Pisum sativum) seed and Arabidopsis thaliana leaf proteomes by the shotgun approach. For this aim, in comprehensive spiking experiments with bovine serum albumin (BSA), we evaluated the linear dynamic range (LDR) of protein quantification in the presence of plant matrices. Furthermore, we addressed the interference of two different plant matrices in quantitative experiments, accomplished with two alternative sample preparation workflows in comparison to conventional FASP-based digestion of cell lysates, considered here as a reference. The spiking experiments revealed high sensitivities (LODs of up to 4 fmol) for spiked BSA and LDRs of at least 0.6 × 10². Thereby, phenol extraction yielded slightly better recoveries, whereas the detergent-based method showed better linearity. Thus, our results indicate the very good applicability of FASP to quantitative plant proteomics with only limited impact of the protein isolation technique on the method's overall performance.

Structural Investigation of Therapeutic Antibodies Using Hydroxyl Radical Protein Footprinting Methods

Commercial monoclonal antibodies are growing and important components of modern therapies against a multitude of human diseases. Well-known high-resolution structural methods such as protein crystallography are often used to characterize antibody structures and to determine paratope and/or epitope binding regions in order to refine antibody design. However, many standard structural techniques require specialized sample preparation that may perturb antibody structure or require high concentrations or other conditions that are far from the conditions conducive to the accurate determination of antigen binding or kinetics. We describe here in this minireview the relatively new method of hydroxyl radical protein footprinting, a solution-state method that can provide structural and kinetic information on antibodies or antibody-antigen interactions useful for therapeutic antibody design. We provide a brief history of hydroxyl radical footprinting, examples of current implementations, and recent advances in throughput and accessibility.

Detergent-Assisted Protein Digestion-On the Way to Avoid the Key Bottleneck of Shotgun Bottom-Up Proteomics

Gel-free bottom-up shotgun proteomics is the principal methodological platform for the state-of-the-art proteome research. This methodology assumes quantitative isolation of the total protein fraction from a complex biological sample, its limited proteolysis with site-specific proteases, analysis of the resulted peptides with nanoscaled reversed-phase high-performance liquid chromatography-(tandem) mass spectrometry (nanoRP-HPLC-MS and MS/MS), protein identification by sequence database search and peptide-based quantitative analysis. The most critical steps of this workflow are protein reconstitution and digestion; therefore, detergents and chaotropic agents are strongly mandatory to ensure complete solubilization of complex protein isolates and to achieve accessibility of all protease cleavage sites. However, detergents are incompatible with both RP separation and electrospray ionization (ESI). Therefore, to make LC-MS analysis possible, several strategies were implemented in the shotgun proteomics workflow. These techniques rely either on enzymatic digestion in centrifugal filters with subsequent evacuation of the detergent, or employment of MS-compatible surfactants, which can be degraded upon the digestion. In this review we comprehensively address all currently available strategies for the detergent-assisted proteolysis in respect of their relative efficiency when applied to different biological matrices. We critically discuss the current progress and the further perspectives of these technologies in the context of its advances and gaps.

Unveiling and application of the chicken egg proteome: An overview on a two-decade achievement

Egg proteins are not only the most complete and ideal form of protein for human or embryo nutrition but also play the vital role in the food industry. Egg proteins are subjected to many potential changes under various conditions, which may further alter the nutritional value, physicochemical-properties, and bioactivities of proteins. Recent advances in our understanding of the proteome of raw egg matrix from different species and dynamic changes occurring during storage and incubation are developing rapidly. This review provides a comprehensive overview of the main characteristics of chicken egg proteome, covering all its components and applications under various conditions, such as markers detection, egg quality evaluation, genetic and biological unknown identification, and embryonic nutritional supplementation, which not only contributes to our in-depth understanding of each constituent functionality of proteome, but also provides information to increase the value to egg industry.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Proteomics as a tool to gain next level insights into photo-crosslinkable biopolymer modifications

The distribution of photo-crosslinkable moieties onto a protein backbone can affect a biomaterial's crosslinking behavior, and therefore also its mechanical and biological properties. A profound insight in this respect is essential for biomaterials exploited in tissue engineering and regenerative medicine. In the present work, photo-crosslinkable moieties have been introduced on the primary amine groups of: (i) a recombinant collagen peptide (RCPhC1) with a known amino acid (AA) sequence, and (ii) bovine skin collagen (COL BS) with an unknown AA sequence. The degree of substitution (DS) was quantified with two conventional techniques: an ortho-phthalic dialdehyde (OPA) assay and 1H NMR spectroscopy. However, neither of both provides information on the exact type and location of the modified AAs. Therefore, for the first time, proteomic analysis was evaluated herein as a tool to identify functionalized AAs as well as the exact position of photo-crosslinkable moieties along the AA sequence, thereby enabling an in-depth, unprecedented characterization of functionalized photo-crosslinkable biopolymers. Moreover, our strategy enabled to visualize the spatial distribution of the modifications within the overall structure of the protein. Proteomics has proven to provide unprecedented insight in the distribution of photo-crosslinkable moieties along the protein backbone, undoubtedly contributing to superior functional biomaterial design to serve regenerative medicine.

Separation methods in single-cell proteomics: RPLC or CE?

Cellular heterogeneity is commonly investigated using single-cell genomics and transcriptomics to investigate biological questions such as disease mechanism, therapeutic screening, and genomic and transcriptomic diversity between cellular populations and subpopulations at the cellular level. Single-cell mass spectrometry (MS)-based proteomics enables the high-throughput examination of protein expression at the single-cell level with wide applicability, and with spatial and temporal resolution, applicable to the study of cellular development, disease, effect of treatment, etc. The study of single-cell proteomics has lagged behind genomics and transcriptomics largely because proteins from single-cell samples cannot be amplified as DNA and RNA can using well established techniques such as PCR. Therefore, analytical methods must be robust, reproducible, and sensitive enough to detect the very small amount of protein within a single cell. To this end, nearly every step of the proteomics process has been extensively altered and improved to facilitate the proteomics analysis of single cells including cell counting and sorting, lysis, protein digestion, sample cleanup, separation, MS data acquisition, and data analysis. Here, we have reviewed recent advances in single-cell protein separation using nano reversed phase liquid chromatography (nRPLC) and capillary electrophoresis (CE) to inform application driven selection of separation techniques in the laboratory setting.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.