Identification of modified peptides using localization-aware open search

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

Singlet oxygen-mediated photochemical cross-linking of an engineered fluorescent flavoprotein iLOV

Genetically-encoded photoactive proteins are integral tools in modern biochemical and molecular biological research. Within this tool box, truncated variants of the phototropin 2 light-oxygen-voltage (LOV) flavoprotein have been developed to photochemically generate singlet oxygen (1O2) in vitro and in vivo, yet the effect of 1O2 on these genetically encoded photosensitizers remains underexplored. In this study, we demonstrate that the "improved" LOV (iLOV) flavoprotein is capable of photochemical 1O2 generation. Once generated, 1O2 induces protein oligomerization via covalent cross-linking. The molecular targets of protein oligomerization by cross-linking are not endogenous tryptophans or tyrosines, but rather primarily histidines. Substitution of surface-exposed histidines for serine or glycine residues effectively eliminates protein cross-linking. When used in biochemical applications, such protein-protein cross-links may interfere with native biological responses to 1O2, which can be ameliorated by substitution of the surface exposed histidines of iLOV or other 1O2-generating flavoproteins.

Improved deconvolution of natural products' protein targets using diagnostic ions from chemical proteomics linkers

Identification of interactions between proteins and natural products or similar active small molecules is crucial for understanding of their mechanism of action on a molecular level. To search elusive, often labile, and low-abundant conjugates between proteins and active compounds, chemical proteomics introduces a feasible strategy that allows to enrich and detect these conjugates. Recent advances in mass spectrometry techniques and search algorithms provide unprecedented depth of proteome coverage and the possibility to detect desired modified peptides with high sensitivity. The chemical 'linker' connecting an active compound-protein conjugate with a detection tag is the critical component of all chemical proteomic workflows. In this review, we discuss the properties and applications of different chemical proteomics linkers with special focus on their fragmentation releasing diagnostic ions and how these may improve the confidence in identified active compound-peptide conjugates. The application of advanced search options improves the identification rates and may help to identify otherwise difficult to find interactions between active compounds and proteins, which may result from unperturbed conditions, and thus are of high physiological relevance.

Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions

Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.

Quantitative Comparison of Deparaffinization, Rehydration, and Extraction Methods for FFPE Tissue Proteomics and Phosphoproteomics

Formalin-fixed paraffin-embedded (FFPE) tissues are suitable for proteomic and phosphoproteomic biomarker studies by data-independent acquisition mass spectrometry. The choice of the sample preparation method influences the number, intensity, and reproducibility of identifications. By comparing four deparaffinization and rehydration methods, including heptane, histolene, SubX, and xylene, we found that heptane and methanol produced the lowest coefficients of variation (CVs). Using this, five extraction methods from the literature were modified and evaluated for their performance using kidney, leg muscle, lung, and testicular rat organs. All methods performed well, except for SP3 due to insufficient tissue lysis. Heat n' Beat was the fastest and most reproducible method with the highest digestion efficiency and lowest CVs. S-Trap produced the highest peptide yield, while TFE produced the best phosphopeptide enrichment efficiency. The quantitation of FFPE-derived peptides remains an ongoing challenge with bias in UV and fluorescence assays across methods, most notably in SPEED. Functional enrichment analysis demonstrated that each method favored extracting some gene ontology cellular components over others including chromosome, cytoplasmic, cytoskeleton, endoplasmic reticulum, membrane, mitochondrion, and nucleoplasm protein groups. The outcome is a set of recommendations for choosing the most appropriate method for different settings.

Posttranslationally modified self-peptides promote hypertension in mouse models

Posttranslational modifications can enhance immunogenicity of self-proteins. In several conditions, including hypertension, systemic lupus erythematosus, and heart failure, isolevuglandins (IsoLGs) are formed by lipid peroxidation and covalently bond with protein lysine residues. Here, we show that the murine class I major histocompatibility complex (MHC-I) variant H-2Db uniquely presents isoLG-modified peptides and developed a computational pipeline that identifies structural features for MHC-I accommodation of such peptides. We identified isoLG-adducted peptides from renal proteins, including sodium glucose transporter 2, cadherin 16, Kelch domain-containing protein 7A, and solute carrier family 23, that are recognized by CD8+ T cells in tissues of hypertensive mice, induce T cell proliferation in vitro, and prime hypertension after adoptive transfer. Finally, we find patterns of isoLG-adducted antigen restriction in class I human leukocyte antigens that are similar to those in murine analogs. Thus, we have used a combined computational and experimental approach to define likely antigenic peptides in hypertension.

Proceedings of the EuBIC-MS developers meeting 2023

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Generating cysteine-trypsin cleavage sites with 2-chloroacetamidine capping

An electrophilic arginine mimetic, 2-chloroacetamidine (CAM), was deployed to enable trypsin-mediated proteolysis at cysteine residues and to enhance mass spectrometry-based proteomic detection of cysteine-containing peptides. Illustrating the value of the CAM-capping strategy, proteogenomic analysis using a two-stage false discovery rate (FDR) search revealed >50% enhanced coverage of missense variants, when compared to established workflows.

The Plant PTM Viewer 2.0: in-depth exploration of plant protein modification landscapes

Post-translational modifications (PTMs) greatly increase protein diversity and functionality. To help the plant research community interpret the ever-increasing number of reported PTMs, the Plant PTM Viewer (https://www.psb.ugent.be/PlantPTMViewer) provides an intuitive overview of plant protein PTMs and the tools to assess it. This update includes 62 novel PTM profiling studies, adding a total of 112 000 modified peptides reporting plant PTMs, including 14 additional PTM types and three species (moss, tomato, and soybean). Furthermore, an open modification re-analysis of a large-scale Arabidopsis thaliana mass spectrometry tissue atlas identified previously uncharted landscapes of lysine acylations predominant in seed and flower tissues and 3-phosphoglycerylation on glycolytic enzymes in plants. An extra 'Protein list analysis' tool was developed for retrieval and assessing the enrichment of PTMs in a protein list of interest. We conducted a protein list analysis on nuclear proteins, revealing a substantial number of redox modifications in the nucleus, confirming previous assumptions regarding the redox regulation of transcription. We encourage the plant research community to use PTM Viewer 2.0 for hypothesis testing and new target discovery, and also to submit new data to expand the coverage of conditions, plant species, and PTM types, thereby enriching our understanding of plant biology.

Two-Step Enrichment Facilitates Background Reduction for Proteomic Analysis of Lysosomes

Lysosomes constitute the main degradative compartment of most mammalian cells and are involved in various cellular functions. Most of them are catalyzed by lysosomal proteins, which typically are low abundant, complicating their analysis by mass spectrometry-based proteomics. To increase analytical performance and to enable profiling of lysosomal content, lysosomes are often enriched. Two approaches have gained popularity in recent years, namely, superparamagnetic iron oxide nanoparticles (SPIONs) and immunoprecipitation from cells overexpressing a 3xHA-tagged version of TMEM192 (TMEM-IP). The effect of these approaches on the lysosomal proteome has not been investigated to date. We addressed this topic through a combination of both techniques and proteomic analysis of lysosome-enriched fractions. For SPIONs treatment, we identified altered cellular iron homeostasis and moderate changes of the lysosomal proteome. For overexpression of TMEM192, we observed more pronounced effects in lysosomal protein expression, especially for lysosomal membrane proteins and those involved in protein trafficking. Furthermore, we established a combined strategy based on the sequential enrichment of lysosomes with SPIONs and TMEM-IP. This enabled increased purity of lysosome-enriched fractions and, through TMEM-IP-based lysosome enrichment from SPIONs flow-through and eluate fractions, additional insights into the properties of individual approaches. All data are available via ProteomeXchange with PXD048696.

Synthesis and biological evaluation of vioprolide B and its dehydrobutyrine-glycine analogue

Herein, we describe the total synthesis of the depsipeptide vioprolide B and of an analogue, in which the (E)-dehydrobutyrine amino acid was replaced by glycine. The compounds were studied in biological assays which revealed cytotoxicity solely for vioprolide B presumably by covalent binding to cysteine residues of elongation factor eEF1A1 and of chromatin assembly factor CHAF1A.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

In-gel protein digestion using acidic methanol produces a highly selective methylation of glutamic acid residues

Mass-tolerant open search methods allow the high-throughput analysis of modified peptides by mass spectrometry. These techniques have paved the way to unbiased analysis of post-translational modifications in biological contexts, as well as of chemical modifications produced during the manipulation of protein samples. In this work, we have analyzed in-depth a wide variety of samples of different biological origin, including cells, extracellular vesicles, secretomes, centrosomes and tissue preparations, using Comet-ReCom, a recently improved version of the open search engine Comet-PTM. Our results demonstrate that glutamic acid residues undergo intensive methyl esterification when protein digestion is performed using in-gel techniques, but not using gel-free approaches. This effect was highly specific to Glu and was not found for other methylable residues such as Asp.

Unraveling the mechanism of small molecule induced activation of Staphylococcus aureus signal peptidase IB

Staphylococcus aureus signal peptidase IB (SpsB) is an essential enzyme for protein secretion. While inhibition of its activity by small molecules is a well-precedented mechanism to kill bacteria, the mode of activation is however less understood. We here investigate the activation mechanism of a recently introduced activator, the antibiotic compound PK150, and demonstrate by combined experimental and Molecular Dynamics (MD) simulation studies a unique principle of enzyme stimulation. Mass spectrometric studies with an affinity-based probe of PK150 unravel the binding site of PK150 in SpsB which is used as a starting point for MD simulations. Our model shows the localization of the molecule in an allosteric pocket next to the active site which shields the catalytic dyad from excess water that destabilizes the catalytic geometry. This mechanism is validated by the placement of mutations aligning the binding pocket of PK150. While the mutants retain turnover of the SpsB substrate, no stimulation of activity is observed upon PK150 addition. Overall, our study elucidates a previously little investigated mechanism of enzyme activation and serves as a starting point for the development of future enzyme activators.

Repurposed 3D Printer Allows Economical and Programmable Fraction Collection for Proteomics of Nanogram Scale Samples

In this work, we describe the construction and application of a repurposed 3D-printer as a fraction collector. We utilize a nano-LC to ensure minimal volumes and surfaces although any LC can be coupled. The setup operates as a high-pH fractionation system capable of effectively working with nanogram scales of lysate digests. The 2D RP-RP system demonstrated superior proteome coverage over single-shot data-dependent acquisition (DDA) analysis using only 5 ng of human cell lysate digest with performance increasing with increasing amounts of material. We found that the fractionation system allowed over 60% signal recovery at the peptide level and, more importantly, we observed improved protein level intensity coverage, which indicates the complexity reduction afforded by the system outweighs the sample losses endured. The application of data-independent acquisition (DIA) and wide window acquisition (WWA) to fractionated samples allowed nearly 8000 proteins to be identified from 50 ng of the material. The utility of the 2D system was further investigated for phosphoproteomics (>21 000 phosphosites from 50 μg starting material) and pull-down type experiments and showed substantial improvements over single-shot experiments. We show that the 2D RP-RP system is a highly versatile and powerful tool for many proteomics workflows.

The mycobacterial glycoside hydrolase LamH enables capsular arabinomannan release and stimulates growth

Mycobacterial glycolipids are important cell envelope structures that drive host-pathogen interactions. Arguably, the most important are lipoarabinomannan (LAM) and its precursor, lipomannan (LM), which are trafficked from the bacterium to the host via unknown mechanisms. Arabinomannan is thought to be a capsular derivative of these molecules, lacking a lipid anchor. However, the mechanism by which this material is generated has yet to be elucidated. Here, we describe the identification of a glycoside hydrolase family 76 enzyme that we term LamH (Rv0365c in Mycobacterium tuberculosis) which specifically cleaves α-1,6-mannoside linkages within LM and LAM, driving its export to the capsule releasing its phosphatidyl-myo-inositol mannoside lipid anchor. Unexpectedly, we found that the catalytic activity of this enzyme is important for efficient exit from stationary phase cultures, potentially implicating arabinomannan as a signal for growth phase transition. Finally, we demonstrate that LamH is important for M. tuberculosis survival in macrophages.

Glycan-Tailored Glycoproteomic Analysis Reveals Serine is the Sole Residue Subjected to O-Linked Glycosylation in Acinetobacter baumannii

Protein glycosylation is a ubiquitous process observed across all domains of life. Within the human pathogen Acinetobacter baumannii, O-linked glycosylation is required for virulence; however, the targets and conservation of glycosylation events remain poorly defined. In this work, we expand our understanding of the breadth and site specificity of glycosylation within A. baumannii by demonstrating the value of strain specific glycan electron-transfer/higher-energy collision dissociation (EThcD) triggering for bacterial glycoproteomics. By coupling tailored EThcD-triggering regimes to complementary glycopeptide enrichment approaches, we assessed the observable glycoproteome of three A. baumannii strains (ATCC19606, BAL062, and D1279779). Combining glycopeptide enrichment techniques including ion mobility (FAIMS), metal oxide affinity chromatography (titanium dioxide), and hydrophilic interaction liquid chromatography (ZIC-HILIC), as well as the use of multiple proteases (trypsin, GluC, pepsin, and thermolysis), we expand the known A. baumannii glycoproteome to 33 unique glycoproteins containing 42 glycosylation sites. We demonstrate that serine is the sole residue subjected to glycosylation with the substitution of serine for threonine abolishing glycosylation in model glycoproteins. An A. baumannii pan-genome built from 576 reference genomes identified that serine glycosylation sites are highly conserved. Combined this work expands our knowledge of the conservation and site specificity of A. baumannii O-linked glycosylation.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

Diverse Gene Regulatory Mechanisms Alter Rattlesnake Venom Gene Expression at Fine Evolutionary Scales

Understanding and predicting the relationships between genotype and phenotype is often challenging, largely due to the complex nature of eukaryotic gene regulation. A step towards this goal is to map how phenotypic diversity evolves through genomic changes that modify gene regulatory interactions. Using the Prairie Rattlesnake (Crotalus viridis) and related species, we integrate mRNA-seq, proteomic, ATAC-seq and whole-genome resequencing data to understand how specific evolutionary modifications to gene regulatory network components produce differences in venom gene expression. Through comparisons within and between species, we find a remarkably high degree of gene expression and regulatory network variation across even a shallow level of evolutionary divergence. We use these data to test hypotheses about the roles of specific trans-factors and cis-regulatory elements, how these roles may vary across venom genes and gene families, and how variation in regulatory systems drive diversity in venom phenotypes. Our results illustrate that differences in chromatin and genotype at regulatory elements play major roles in modulating expression. However, we also find that enhancer deletions, differences in transcription factor expression, and variation in activity of the insulator protein CTCF also likely impact venom phenotypes. Our findings provide insight into the diversity and gene-specificity of gene regulatory features and highlight the value of comparative studies to link gene regulatory network variation to phenotypic variation.

Combining FAIMS based glycoproteomics and DIA proteomics reveals widespread proteome alterations in response to glycosylation occupancy changes in Neisseria gonorrhoeae

Protein glycosylation is increasingly recognized as a common protein modification across bacterial species. Within the Neisseria genus O-linked protein glycosylation is conserved yet closely related Neisseria species express O-oligosaccharyltransferases (PglOs) with distinct targeting activities. Within this work, we explore the targeting capacity of different PglOs using Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) fractionation and Data-Independent Acquisition (DIA) to allow the characterization of the impact of changes in glycosylation on the proteome of Neisseria gonorrhoeae. We demonstrate FAIMS expands the known glycoproteome of wild type N. gonorrhoeae MS11 and enables differences in glycosylation to be assessed across strains expressing different pglO allelic chimeras with unique substrate targeting activities. Combining glycoproteomic insights with DIA proteomics, we demonstrate that alterations within pglO alleles have widespread impacts on the proteome of N. gonorrhoeae. Examination of peptides known to be targeted by glycosylation using DIA analysis supports alterations in glycosylation occupancy occurs independently of changes in protein levels and that the occupancy of glycosylation is generally low on most glycoproteins. This work thus expands our understanding of the N. gonorrhoeae glycoproteome and the roles that pglO allelic variation may play in governing genus-level protein glycosylation.

deTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets

MOTIVATION

Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry.

RESULTS

Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions.

AVAILABILITY AND IMPLEMENTATION

deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

A Novel Dry-Cured Ham Broth-Derived Peptide JHBp2 Effectively Inhibits Salmonella typhimurium In Vitro: Integrated Metabolomic, Proteomic, and Molecular Simulation Analyses

JHBp2 is a peptide purified from Jinhua ham broth with antibacterial activity against Salmonella typhimurium. Untargeted metabolomics and label-free quantitative proteomics were used to analyze metabolic and protein expression changes in S. typhimurium after JHBp2 treatment. Cell wall and membrane damage results indicate that JHBp2 has membrane-disruptive properties, causing leakage of intracellular nucleic acids and proteins. Metabolomics revealed 516 differentially expressed metabolites, involving cofactor biosynthesis, purine metabolism, ABC transporters, glutathione metabolism, pyrimidine metabolism, etc. Proteomics detected 735 differentially expressed proteins, involving pyruvate metabolism, amino acid biosynthesis, purine metabolism, carbon metabolism, glycolysis/gluconeogenesis, etc. RT-qPCR and proteomics results showed a positive correlation, and molecular docking demonstrated stable binding of JHBp2 to some differentially expressed proteins. In summary, JHBp2 could disrupt the S. typhimurium cell wall and membrane structure, interfere with synthesis of membrane-related proteins, trigger intracellular substance leak, and reduce levels of enzymes and metabolites involved in energy metabolism, amino acid anabolism, and nucleotide anabolism.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

Proteomic changes in the human cerebrovasculature in Alzheimer's disease and related tauopathies linked to peripheral biomarkers in plasma and cerebrospinal fluid

INTRODUCTION

Cerebrovascular dysfunction is a pathological hallmark of Alzheimer's disease (AD). Nevertheless, detecting cerebrovascular changes within bulk tissues has limited our ability to characterize proteomic alterations from less abundant cell types.

METHODS

We conducted quantitative proteomics on bulk brain tissues and isolated cerebrovasculature from the same individuals, encompassing control (N = 28), progressive supranuclear palsy (PSP) (N = 18), and AD (N = 21) cases.

RESULTS

Protein co-expression network analysis identified unique cerebrovascular modules significantly correlated with amyloid plaques, cerebrovascular amyloid angiopathy (CAA), and/or tau pathology. The protein products within AD genetic risk loci were concentrated within cerebrovascular modules. The overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with cerebrovascular network highlighted a significant increase of matrisome proteins, SMOC1 and SMOC2, in CSF, plasma, and brain.

DISCUSSION

These findings enhance our understanding of cerebrovascular deficits in AD, shedding light on potential biomarkers associated with CAA and vascular dysfunction in neurodegenerative diseases.

Assessing Target Specificity of the Small Molecule Inhibitor MARIMASTAT to Snake Venom Toxins: A Novel Application of Thermal Proteome Profiling

New treatments that circumvent the pitfalls of traditional antivenom therapies are critical to address the problem of snakebite globally. Numerous snake venom toxin inhibitors have shown promising cross-species neutralization of medically significant venom toxins in vivo and in vitro. The development of high-throughput approaches for the screening of such inhibitors could accelerate their identification, testing, and implementation and thus holds exciting potential for improving the treatments and outcomes of snakebite envenomation worldwide. Energetics-based proteomic approaches, including thermal proteome profiling and proteome integral solubility alteration (PISA) assays, represent "deep proteomics" methods for high throughput, proteome-wide identification of drug targets and ligands. In the following study, we apply thermal proteome profiling and PISA methods to characterize the interactions between venom toxin proteoforms in Crotalus atrox (Western Diamondback Rattlesnake) and the snake venom metalloprotease (SVMP) inhibitor marimastat. We investigate its venom proteome-wide effects and characterize its interactions with specific SVMP proteoforms, as well as its potential targeting of non-SVMP venom toxin families. We also compare the performance of PISA thermal window and soluble supernatant with insoluble precipitate using two inhibitor concentrations, providing the first demonstration of the utility of a sensitive high-throughput PISA-based approach to assess the direct targets of small molecule inhibitors for snake venom.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.

Detection of differential bait proteoforms through immunoprecipitation-mass spectrometry data analysis

Proteins are often referred to as the workhorses of cells, and their interactions are necessary to facilitate specific cellular functions. Despite the recognition that protein-protein interactions, and thus protein functions, are determined by proteoform states, such as mutations and post-translational modifications (PTMs), methods for determining the differential abundance of proteoforms across conditions are very limited. Classically, immunoprecipitation coupled with mass spectrometry (IP-MS) has been used to understand how the interactome (preys) of a given protein (bait) changes between conditions to elicit specific cellular functions. Reversing this concept, we present here a new workflow for IP-MS data analysis that focuses on identifying the differential peptidoforms of the bait protein between conditions. This method can provide detailed information about specific bait proteoforms, potentially revealing pathogenic protein states that can be exploited for the development of targeted therapies.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Mass Spectrometry-Based Proteomics of Poly(methylmethacrylate)-Embedded Bone

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely employed technique in proteomics research for studying the proteome biology of various clinical samples. Hard tissues, such as bone and teeth, are routinely preserved using synthetic poly(methyl methacrylate) (PMMA) embedding resins that enable histological, immunohistochemical, and morphological examination. However, the suitability of PMMA-embedded hard tissues for large-scale proteomic analysis remained unexplored. This study is the first to report on the feasibility of PMMA-embedded bone samples for LC-MS/MS analysis. Conventional workflows yielded merely limited coverage of the bone proteome. Using advanced strategies of prefractionation by high-pH reversed-phase liquid chromatography in combination with isobaric tandem mass tag labeling resulted in proteome coverage exceeding 1000 protein identifications. The quantitative comparison with cryopreserved samples revealed that each sample preparation workflow had a distinct impact on the proteomic profile. However, workflow replicates exhibited a high reproducibility for PMMA-embedded samples. Our findings further demonstrate that decalcification prior to protein extraction, along with the analysis of solubilization fractions, is not preferred for PMMA-embedded bone. The biological applicability of the proposed workflow was demonstrated using samples of human PMMA-embedded alveolar bone and the iliac crest, which revealed anatomical site-specific proteomic profiles. Overall, these results establish a crucial foundation for large-scale proteomics studies contributing to our knowledge of bone biology.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics

Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities betweenEscherichia coli and Salmonella typhimurium to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying S. typhimurium peptide-spectra with only the E. coli genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses.

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Structure-function analysis of the cyclic β-1,2-glucan synthase from Agrobacterium tumefaciens

The synthesis of complex sugars is a key aspect of microbial biology. Cyclic β-1,2-glucan (CβG) is a circular polysaccharide critical for host interactions of many bacteria, including major pathogens of humans (Brucella) and plants (Agrobacterium). CβG is produced by the cyclic glucan synthase (Cgs), a multi-domain membrane protein. So far, its structure as well as the mechanism underlining the synthesis have not been clarified. Here we use cryo-electron microscopy (cryo-EM) and functional approaches to study Cgs from A. tumefaciens. We determine the structure of this complex protein machinery and clarify key aspects of CβG synthesis, revealing a distinct mechanism that uses a tyrosine-linked oligosaccharide intermediate in cycles of polymerization and processing of the glucan chain. Our research opens possibilities for combating pathogens that rely on polysaccharide virulence factors and may lead to synthetic biology approaches for producing complex cyclic sugars.

Heparin-enriched plasma proteome is significantly altered in Alzheimer's Disease

Introduction

Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer's disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches.

Methods

We employed heparin affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to capture and enrich HBPs in plasma obtained from AD (n=62) and control (n=47) samples. These profiles were then correlated to a consensus AD brain proteome, as well as with Aβ, tau and phosphorylated tau (pTau) CSF biomarkers from the same individuals. We then leveraged published human postmortem brain proteome datasets to assess the overlap with the heparin-enriched plasma proteome.

Results

Heparin-enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundance proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude were detectable. Utilizing a consensus AD brain protein co-expression network, we observed that specific plasma HBPs exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes highlighting the complex interplay between the two compartments. Elevated HBPs in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate within Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and APOE. Additionally, heparin enriched plasma proteins demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau from the same individuals.

Conclusion

These findings support the utility of a heparin-affinity approach for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.

Identification of structural and regulatory cell-shape determinants in Haloferax volcanii

Archaea play indispensable roles in global biogeochemical cycles, yet many crucial cellular processes, including cell-shape determination, are poorly understood. Haloferax volcanii, a model haloarchaeon, forms rods and disks, depending on growth conditions. Here, we used a combination of iterative proteomics, genetics, and live-cell imaging to identify mutants that only form rods or disks. We compared the proteomes of the mutants with wild-type cells across growth phases, thereby distinguishing between protein abundance changes specific to cell shape and those related to growth phases. The results identified a diverse set of proteins, including predicted transporters, transducers, signaling components, and transcriptional regulators, as important for cell-shape determination. Through phenotypic characterization of deletion strains, we established that rod-determining factor A (RdfA) and disk-determining factor A (DdfA) are required for the formation of rods and disks, respectively. We also identified structural proteins, including an actin homolog that plays a role in disk-shape morphogenesis, which we named volactin. Using live-cell imaging, we determined volactin's cellular localization and showed its dynamic polymerization and depolymerization. Our results provide insights into archaeal cell-shape determination, with possible implications for understanding the evolution of cell morphology regulation across domains.

Cryo-EM structure of the extracellular domain of murine Thrombopoietin Receptor in complex with Thrombopoietin

Thrombopoietin (Tpo) is the primary regulator of megakaryocyte and platelet numbers and is required for haematopoetic stem cell maintenance. Tpo functions by binding its receptor (TpoR, a homodimeric Class I cytokine receptor) and initiating cell proliferation or differentiation. Here we characterise the murine Tpo:TpoR signalling complex biochemically and structurally, using cryo-electron microscopy. Tpo uses opposing surfaces to recruit two copies of receptor, forming a 1:2 complex. Although it binds to the same, membrane-distal site on both receptor chains, it does so with significantly different affinities and its highly glycosylated C-terminal domain is not required. In one receptor chain, a large insertion, unique to TpoR, forms a partially structured loop that contacts cytokine. Tpo binding induces the juxtaposition of the two receptor chains adjacent to the cell membrane. The therapeutic agent romiplostim also targets the cytokine-binding site and the characterisation presented here supports the future development of improved TpoR agonists.

AdductHunter: identifying protein-metal complex adducts in mass spectra

Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that  automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

Cu-Catalyzed Azide-Alkyne-Thiol Reaction Forms Ubiquitous Background in Chemical Proteomic Studies

We report here a Cu-catalyzed azide-alkyne-thiol reaction forming thiotriazoles as the major byproduct under widely used bio-orthogonal protein labeling "click" conditions. The development of Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) had a tremendous impact on many biological discoveries. However, the considered chemoselectivity of CuAAC is hampered by the high reactivity of cysteine free thiols, yielding thiotriazole protein conjugates. The reaction byproducts generate false-positive protein hits in functional proteomic studies. The reported detail investigation of conjugates between chemical probes containing terminal alkynes, azide tags, and cell lysates reveals the formation of thiotriazoles, which can be readily detected by in-gel fluorescence scanning or after peptide and protein enrichment by mass spectrometry-based proteomics. In protein level identification and quantification experiments, the produced fluorescent bands or enriched proteins may not result from the important enzymatically driven reaction and can be falsely assigned as hits. This study provides a complete list of the most common background proteins. The knowledge of this previously overlooked reactivity now leads to the introduction of modified CuAAC conditions, which avoids the undesired product formation, diminishes the background, and hence improves the signal-to-noise ratio.

Oxidative Post-translational Protein Modifications upon Ischemia/Reperfusion Injury

While reperfusion, or restoration of coronary blood flow in acute myocardial infarction, is a requisite for myocardial salvage, it can paradoxically induce a specific damage known as ischemia/reperfusion (I/R) injury. Our understanding of the precise pathophysiological molecular alterations leading to I/R remains limited. In this study, we conducted a comprehensive and unbiased time-course analysis of post-translational modifications (PTMs) in the post-reperfused myocardium of two different animal models (pig and mouse) and evaluated the effect of two different cardioprotective therapies (ischemic preconditioning and neutrophil depletion). In pigs, a first wave of irreversible oxidative damage was observed at the earliest reperfusion time (20 min), impacting proteins essential for cardiac contraction. A second wave, characterized by irreversible oxidation on different residues and reversible Cys oxidation, occurred at late stages (6-12 h), affecting mitochondrial, sarcomere, and inflammation-related proteins. Ischemic preconditioning mitigated the I/R damage caused by the late oxidative wave. In the mouse model, the two-phase pattern of oxidative damage was replicated, and neutrophil depletion mitigated the late wave of I/R-related damage by preventing both Cys reversible oxidation and irreversible oxidation. Altogether, these data identify protein PTMs occurring late after reperfusion as an actionable therapeutic target to reduce the impact of I/R injury.

Proteomic Changes in the Human Cerebrovasculature in Alzheimer's Disease and Related Tauopathies Linked to Peripheral Biomarkers in Plasma and Cerebrospinal Fluid

Dysfunction of the neurovascular unit stands as a significant pathological hallmark of Alzheimer's disease (AD) and age-related neurodegenerative diseases. Nevertheless, detecting vascular changes in the brain within bulk tissues has proven challenging, limiting our ability to characterize proteomic alterations from less abundant cell types. To address this challenge, we conducted quantitative proteomic analyses on both bulk brain tissues and cerebrovascular-enriched fractions from the same individuals, encompassing cognitively unimpaired control, progressive supranuclear palsy (PSP), and AD cases. Protein co-expression network analysis identified modules unique to the cerebrovascular fractions, specifically enriched with pericytes, endothelial cells, and smooth muscle cells. Many of these modules also exhibited significant correlations with amyloid plaques, cerebral amyloid angiopathy (CAA), and/or tau pathology in the brain. Notably, the protein products within AD genetic risk loci were found concentrated within modules unique to the vascular fractions, consistent with a role of cerebrovascular deficits in the etiology of AD. To prioritize peripheral AD biomarkers associated with vascular dysfunction, we assessed the overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with a vascular-enriched network modules in the brain. This analysis highlighted matrisome proteins, SMOC1 and SMOC2, as being increased in CSF, plasma, and brain. Immunohistochemical analysis revealed SMOC1 deposition in both parenchymal plaques and CAA in the AD brain, whereas SMOC2 was predominantly localized to CAA. Collectively, these findings significantly enhance our understanding of the involvement of cerebrovascular abnormalities in AD, shedding light on potential biomarkers and molecular pathways associated with CAA and vascular dysfunction in neurodegenerative diseases.

Automated Immunoprecipitation Workflow for Comprehensive Acetylome Analysis

Immunoprecipitation is one of the most effective methods for enrichment of lysine-acetylated peptides for comprehensive acetylome analysis using mass spectrometry. Manual acetyl peptide enrichment method using non-conjugated antibodies and agarose beads has been developed and applied in various studies. However, it is time-consuming and can introduce contaminants and variability that leads to potential sample loss and decreased sensitivity and robustness of the analysis. Here we describe a fast, automated enrichment protocol that enables reproducible and comprehensive acetylome analysis using a magnetic bead-based immunoprecipitation reagent.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 μl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

A Tip-Based Workflow for Sensitive IMAC-Based Low Nanogram Level Phosphoproteomics

Analyzing the phosphoproteome at nanoscale poses a significant challenge, mainly due to the substantial sample loss from nonspecific surface adsorption during the enrichment of low stoichiometric phosphopeptides. Here, we describe a tandem tip-based phosphoproteomics sample preparation method capable of sequential sample cleanup and enrichment without the need for additional sample transfer, thereby minimizing sample loss. Integration of this method to our recently developed SOP (surfactant-assisted one-pot sample preparation) and iBASIL (improved boosting to amplify signal with isobaric labeling) approaches creates a streamlined workflow, enabling sensitive, high-throughput nanoscale phosphoproteomics measurements.

Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls

Tandem mass spectrometry (MS/MS) facilitates the rapid identification of posttranslational modifications (PTMs), which play a pivotal role in regulating numerous biological processes. This chapter explores recent advancements that expand the types of detectable PTMs and enhance the speed of the PTM searches. We also delve into computational challenges associated with searching for a multitude of PTMs simultaneously. The latter section introduces an automated procedure to identify an extensive range of PTMs using MODplus, a free PTM analysis software tool. We guide the reader through the preparation of the modification search, the determination of optional search parameters, the execution of the search, and the analysis of results, exemplified by a case study using specific MS/MS dataset.