Fast multi-blind modification search through tandem mass spectrometry

Differential prolyl hydroxylation by six Physcomitrella prolyl-4 hydroxylases

Hydroxylation of prolines to 4-trans-hydroxyproline (Hyp) is mediated by prolyl-4 hydroxylases (P4Hs). In plants, Hyps occur in Hydroxyproline-rich glycoproteins (HRGPs), and are frequently O-glycosylated. While both modifications are important, e.g. for cell wall stability, they are undesired in plant-made pharmaceuticals. Sequence motifs for prolyl-hydroxylation were proposed but did not include data from mosses, such as Physcomitrella. We identified six moss P4Hs by phylogenetic reconstruction. Our analysis of 73 Hyps in 24 secretory proteins from multiple mass spectrometry datasets revealed that prolines near other prolines, alanine, serine, threonine and valine were preferentially hydroxylated. About 95 % of Hyps were predictable with combined established methods. In our data, AOV was the most frequent pattern. A combination of 443 AlphaFold models and MS data with 3000 prolines found Hyps mainly on protein surfaces in disordered regions. Moss-produced human erythropoietin (EPO) exhibited O-glycosylation with arabinose chains on two Hyps. This modification was significantly reduced in a p4h1 knock-out (KO) Physcomitrella mutant. Quantitative proteomics with different p4h mutants revealed specific changes in protein amounts, and a modified prolyl-hydroxylation pattern, suggesting a differential function of the Physcomitrella P4Hs. Quantitative RT-PCR revealed a differential effect of single p4h KOs on the expression of the other five p4h genes, suggesting a partial compensation of the mutation. AlphaFold-Multimer models for Physcomitrella P4H1 and its target EPO peptide superposed with the crystal structure of Chlamydomonas P4H1 suggested significant amino acids in the active centre of the enzyme and revealed differences between P4H1 and the other Physcomitrella P4Hs.

Revamping Hepatocellular Carcinoma Immunotherapy: The Advent of Microbial Neoantigen Vaccines

Immunotherapy has revolutionized the treatment paradigm for hepatocellular carcinoma (HCC). However, its efficacy varies significantly with each patient's genetic composition and the complex interactions with their microbiome, both of which are pivotal in shaping anti-tumor immunity. The emergence of microbial neoantigens, a novel class of tumor vaccines, heralds a transformative shift in HCC therapy. This review explores the untapped potential of microbial neoantigens as innovative tumor vaccines, poised to redefine current HCC treatment modalities. For instance, neoantigens derived from the microbiome have demonstrated the capacity to enhance anti-tumor immunity in colorectal cancer, suggesting similar applications in HCC. By harnessing these unique neoantigens, we propose a framework for a personalized immunotherapeutic response, aiming to deliver a more precise and potent treatment strategy for HCC. Leveraging these neoantigens could significantly advance personalized medicine, potentially revolutionizing patient outcomes in HCC therapy.

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.

RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.

AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence

The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.

Conjugation site characterization of antibody-drug conjugates using electron-transfer/higher-energy collision dissociation (EThcD)

Antibody-drug conjugates (ADCs) are formed by binding of cytotoxic drugs to monoclonal antibodies (mAbs) through chemical linkers. A comprehensive evaluation of the critical quality attributes (CQAs) of ADCs is vital for drug development but remains challenging owing to ADC structural heterogeneity than mAbs. Drug conjugation sites can considerably affect ADC properties, such as stability and pharmacokinetics, however, few studies have focused on method development in this area owing to technical challenges. Hybrid electron-transfer/higher-energy collision dissociation (EThcD) produces more fragment ions than conventional higher-energy collision dissociation (HCD) fragmentation, which aids in identifying and localizing post-translational modifications. Herein, we systematically employ EThcD to assess the fragmentation mode impact on conjugation site characterization for randomly conjugated and site-specific ADCs. EThcD generates more fragment ions in tandem mass spectrometry (MS/MS) spectra compared with HCD. Additional ions aid in pinpointing the correct conjugation sites that bear complex linker payload structures. Our study may contribute to the quality control of various preclinical and clinical ADCs.

Challenges and opportunities in analyzing and modeling peptide presentation by HLA-II proteins

The human leukocyte antigen (HLA) proteins are an indispensable component of adaptive immunity because of their role in presenting self and foreign peptides to T cells. Further, many complex diseases are associated with genetic variation in the HLA region, implying an important role for specific HLA-presented peptides in the etiology of these diseases. Identifying the specific set of peptides presented by an individual’s HLA proteins in vivo, as a whole being referred to as the immunopeptidome, has therefore gathered increasing attention for different reasons. For example, identifying neoepitopes for cancer immunotherapy, vaccine development against infectious pathogens, or elucidating the role of HLA in autoimmunity. Despite the tremendous progress made during the last decade in these areas, several questions remain unanswered. In this perspective, we highlight five remaining key challenges in the analysis of peptide presentation and T cell immunogenicity and discuss potential solutions to these problems. We believe that addressing these questions would not only improve our understanding of disease etiology but will also have a direct translational impact in terms of engineering better vaccines and in developing more potent immunotherapies.

The ProteomeXchange consortium at 10 years: 2023 update

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler

Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail, and (5) verify that the hyperparameter estimations are sensible.

Understanding emerging bioactive metabolites with putative roles in cancer biology

Dysregulated metabolism in cancers is, by now, well established. Although metabolic adaptations provide cancers with the ability to synthesize the precursors required for rapid biosynthesis, some metabolites have direct functional, or bioactive, effects in human cells. Here we summarize recently identified metabolites that have bioactive roles either as post-translational modifications (PTMs) on proteins or in, yet unknown ways. We propose that these metabolites could play a bioactive role in promoting or inhibiting cancer cell phenotypes in a manner that is mostly unexplored. To study these potentially important bioactive roles, we discuss several novel metabolomic and proteomic approaches aimed at defining novel PTMs and metabolite-protein interactions. Understanding metabolite PTMs and protein interactors of bioactive metabolites may provide entirely new therapeutic targets for cancer.

Mass Spectrometry Approaches Empowering Neuropeptide Discovery and Therapeutics

The discovery of insulin in the early 1900s ushered in the era of research related to peptides acting as hormones and neuromodulators, among other regulatory roles. These essential gene products are found in all organisms, from the most primitive to the most evolved, and carry important biologic information that coordinates complex physiology and behavior; their misregulation has been implicated in a variety of diseases. The evolutionary origins of at least 30 neuropeptide signaling systems have been traced to the common ancestor of protostomes and deuterostomes. With the use of relevant animal models and modern technologies, we can gain mechanistic insight into orthologous and paralogous endogenous peptides and translate that knowledge into medically relevant insights and new treatments. Groundbreaking advances in medicine and basic science influence how signaling peptides are defined today. The precise mechanistic pathways for over 100 endogenous peptides in mammals are now known and have laid the foundation for multiple drug development pipelines. Peptide biologics have become valuable drugs due to their unique specificity and biologic activity, lack of toxic metabolites, and minimal undesirable interactions. This review outlines modern technologies that enable neuropeptide discovery and characterization, and highlights lessons from nature made possible by neuropeptide research in relevant animal models that is being adopted by the pharmaceutical industry. We conclude with a brief overview of approaches/strategies for effective development of peptides as drugs. SIGNIFICANCE STATEMENT: Neuropeptides, an important class of cell-cell signaling molecules, are involved in maintaining a range of physiological functions. Since the discovery of insulin's activity, over 100 bioactive peptides and peptide analogs have been used as therapeutics. Because these are complex molecules not easily predicted from a genome and their activity can change with subtle chemical modifications, mass spectrometry (MS) has significantly empowered peptide discovery and characterization. This review highlights contributions of MS-based research towards the development of therapeutic peptides.

pH Dependence of Succinimide-Ester-Based Protein Cross-Linking for Structural Mass Spectrometry Applications

Within the research field of cross-linking mass spectrometry (XL-MS), the most commonly used cross-linking reagents are succinimide-ester-based (e.g., disuccinimidyl suberate (DSS)). These reagents primarily cross-link lysine side chains. So far, they have predominantly been used to investigate protein structures at neutral to slightly basic pH (7.0-8.5) to ensure the reactivity of the primary amine of the lysine side chain. However, disease-related molecular processes are not limited to such pH ranges; e.g., some important biological pathways are active in acidic intracellular compartments. The applicability of lysine-reactive cross-linking reagents to low-pH conditions remains unclear. Here, we cross-linked a mixture of eight model proteins at eight different pH conditions (pH 4.0-7.5) to investigate the pH dependency of DSS. DSS was able to cross-link proteins even at pH 4.0, but a clear decrease in the cross-linking efficiency was observed when the pH was lowered. Nevertheless, at pH 5.0, approximately half of the number of cross-links observed at pH 7.5 could still be identified. These findings highlight the ability of succinimide-based cross-linking reagents to be useful in probing the structure of proteins in a slightly acidic environment.

Discovery and Visualization of Uncharacterized Drug-Protein Adducts Using Mass Spectrometry

Drugs are often metabolized to reactive intermediates that form protein adducts. Adducts can inhibit protein activity, elicit immune responses, and cause life-threatening adverse drug reactions. The masses of reactive metabolites are frequently unknown, rendering traditional mass spectrometry-based proteomics approaches incapable of adduct identification. Here, we present Magnum, an open-mass search algorithm optimized for adduct identification, and Limelight, a web-based data processing package for analysis and visualization of data from all existing algorithms. Limelight incorporates tools for sample comparisons and xenobiotic-adduct discovery. We validate our tools with three drug/protein combinations and apply our label-free workflow to identify novel xenobiotic-protein adducts in CYP3A4. Our new methods and software enable accurate identification of xenobiotic-protein adducts with no prior knowledge of adduct masses or protein targets. Magnum outperforms existing label-free tools in xenobiotic-protein adduct discovery, while Limelight fulfills a major need in the rapidly developing field of open-mass searching, which until now lacked comprehensive data visualization tools.

A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium

The enormous chemical diversity and strain variability of prokaryotic protein glycosylation makes their large-scale exploration exceptionally challenging. Therefore, despite the universal relevance of protein glycosylation across all domains of life, the understanding of their biological significance and the evolutionary forces shaping oligosaccharide structures remains highly limited. Here, we report on a newly established mass binning glycoproteomics approach that establishes the chemical identity of the carbohydrate components and performs untargeted exploration of prokaryotic oligosaccharides from large-scale proteomics data directly. We demonstrate our approach by exploring an enrichment culture of the globally relevant anaerobic ammonium-oxidizing bacterium Ca. Kuenenia stuttgartiensis. By doing so we resolve a remarkable array of oligosaccharides, which are produced by two seemingly unrelated biosynthetic routes, and which modify the same surface-layer protein simultaneously. More intriguingly, the investigated strain also accomplished modulation of highly specialized sugars, supposedly in response to its energy metabolism-the anaerobic oxidation of ammonium-which depends on the acquisition of substrates of opposite charges. Ultimately, we provide a systematic approach for the compositional exploration of prokaryotic protein glycosylation, and reveal a remarkable example for the evolution of complex oligosaccharides in bacteria.

Discovery of Unknown Posttranslational Modifications by Top-Down Mass Spectrometry

Protein encoding genes can undergo modifications posttranscriptionally and posttranslationally, yielding many different "proteoforms." The chemical diversity of such modifications is known to be important biomarkers of function within biological systems but is not completely understood. Top-down mass spectrometry is a valuable tool for the characterization of proteoforms, especially for histones that have complex combinations of posttranslational modifications (PTMs). In this chapter, we present a top-down liquid chromatography-mass spectrometry experimental and data analysis workflow for the identification of novel, unexpected modifications on histones. Proteoforms of interest are first discovered using the "open" modification search in TopPIC. Then target proteoforms are manually confirmed using the data visualization tool-LcMsSpectator, part of the Informed-Proteomics package. The workflow can be very helpful in targeted PTM analysis and can be expanded to other types of proteins for discovery of unknown PTMs.

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

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

Understanding the Limit of Open Search in the Identification of Peptides With Post-translational Modifications - A Simulation-Based Study

Peptide identification from tandem mass spectrometry data is a fundamental task in computational proteomics. Traditional algorithms perform well when facing unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, open search methods have been proposed to identify peptides with PTMs. While the performance of these new methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and the quality parameters of tandem mass spectrometry data as well as the number of PTMs in peptides. In this paper, we have proposed an analytical model derived from simulated data to describe the relationship between the probability of obtaining correct results and the spectrum quality as well as the number of PTMs. The proposed model is verified using 1,464,146 real experimental spectra. The consistent trend observed in both simulated data and real data reveals the necessary conditions to effectively apply open search methods. Source code of our study is available at http://bioinformatics.ust.hk/PST.html.

Proteogenomic Analysis Provides Novel Insight into Genome Annotation and Nitrogen Metabolism in Nostoc sp. PCC 7120

Cyanobacteria, capable of oxygenic photosynthesis, play a vital role in nitrogen and carbon cycles. Nostoc sp. PCC 7120 (Nostoc 7120) is a model cyanobacterium commonly used to study cell differentiation and nitrogen metabolism. Although its genome was released in 2002, a high-quality genome annotation remains unavailable for this model cyanobacterium. Therefore, in this study, we performed an in-depth proteogenomic analysis based on high-resolution mass spectrometry (MS) data to refine the genome annotation of Nostoc 7120. We unambiguously identified 5,519 predicted protein-coding genes and revealed 26 novel genes, 75 revised genes, and 27 different kinds of posttranslational modifications in Nostoc 7120. A subset of these novel proteins were further validated at both the mRNA and peptide levels. Functional analysis suggested that many newly annotated proteins may participate in nitrogen or cadmium/mercury metabolism in Nostoc 7120. Moreover, we constructed an updated Nostoc 7120 database based on our proteogenomic results and presented examples of how the updated database could be used to improve the annotation of proteomic data. Our study provides the most comprehensive annotation of the Nostoc 7120 genome thus far and will serve as a valuable resource for the study of nitrogen metabolism in Nostoc 7120.

IMPORTANCE Cyanobacteria are a large group of prokaryotes capable of oxygenic photosynthesis and play a vital role in nitrogen and carbon cycles on Earth. Nostoc 7120 is a commonly used model cyanobacterium for studying cell differentiation and nitrogen metabolism. In this study, we presented the first comprehensive draft map of the Nostoc 7120 proteome and a wide range of posttranslational modifications. In addition, we constructed an updated database of Nostoc 7120 based on our proteogenomic results and presented examples of how the updated database could be used for system-level studies of Nostoc 7120. Our study provides the most comprehensive annotation of Nostoc 7120 genome and a valuable resource for the study of nitrogen metabolism in this model cyanobacterium.

Photoactivatable ribonucleosides mark base-specific RNA-binding sites

RNA-protein interaction can be captured by crosslinking and enrichment followed by tandem mass spectrometry, but it remains challenging to pinpoint RNA-binding sites (RBSs) or provide direct evidence for RNA-binding. To overcome these limitations, we here developed pRBS-ID, by incorporating the benefits of UVA-based photoactivatable ribonucleoside (PAR; 4-thiouridine and 6-thioguanosine) crosslinking and chemical RNA cleavage. pRBS-ID robustly detects peptides crosslinked to PAR adducts, offering direct RNA-binding evidence and identifying RBSs at single amino acid-resolution with base-specificity (U or G). Using pRBS-ID, we could profile uridine-contacting RBSs globally and discover guanosine-contacting RBSs, which allowed us to characterize the base-specific interactions. We also applied the search pipeline to analyze the datasets from UVC-based RBS-ID experiments, altogether offering a comprehensive list of human RBSs with high coverage (3,077 RBSs in 532 proteins in total). pRBS-ID is a widely applicable platform to investigate the molecular basis of posttranscriptional regulation.

AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results

Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat. SIGNIFICANCE: Mass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.

Bcl-2-associated transcription factor 1 Ser290 phosphorylation mediates DNA damage response and regulates radiosensitivity in gastric cancer

BACKGROUND

DNA damage response plays critical roles in tumor pathogenesis and radiotherapy resistance. Protein phosphorylation is a critical mechanism in regulation of DNA damage response; however, the key mediators for radiosensitivity in gastric cancer still needs further exploration.

METHODS

A quick label-free phosphoproteomics using high-resolution mass spectrometry and an open search approach was applied to paired tumor and adjacent tissues from five patients with gastric cancer. The dysregulated phosphoproteins were identified and their associated-pathways analyzed using Gene Set Enrichment Analysis (GSEA). The mostly regulated phosphoproteins and their potential functions were validated by the specific antibodies against the phosphorylation sites. Specific protein phosphorylation was further analyzed by functional and clinical approaches.

RESULTS

832 gastric cancer-associated unique phosphorylated sites were identified, among which 25 were up- and 52 down-regulated. Markedly, the dysregulated phosphoproteins were primarily enriched in DNA-damage-response-associated pathways. Particularly, the phosphorylation of Bcl-2-associated transcription factor 1 (BCLAF1) at Ser290 was significantly upregulated in tumor. The upregulation of BCLAF1 Ser290 phosphorylation (pBCLAF1 (Ser290)) in tumor was confirmed by tissue microarray studies and further indicated in association with poor prognosis of gastric cancer patients. Eliminating the phosphorylation of BCLAF1 at Ser290 suppressed gastric cancer (GC) cell proliferation. Upregulation of pBCLAF1 (Ser290) was found in association with irradiation-induced γ-H2AX expression in the nucleus, leading to an increased DNA damage repair response, and a marked inhibition of irradiation-induced cancer cell apoptosis.

CONCLUSIONS

The phosphorylation of BCLAF1 at Ser290 is involved in the regulation of DNA damage response, indicating an important target for the resistance of radiotherapy.

Decoding post translational modification crosstalk with proteomics

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

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Description of basic modules and different modes of PTM crosstalk.

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

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

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Future directions and potential proteomic methods for elucidating PTM crosstalk.

New insights into the mechanisms of age-related protein-protein crosslinking in the human lens

Although protein crosslinking is often linked with aging as well as some age-related diseases, very few molecular details are available on the nature of the amino acids involved, or mechanisms that are responsible for crosslinking. Recent research has shown that several amino acids are able to generate reactive intermediates that ultimately lead to covalent crosslinking through multiple non-enzymatic mechanisms. This information has been derived from proteomic investigations on aged human lenses and the mechanisms of crosslinking, in each case, have been elucidated using model peptides. Residues involved in spontaneous protein-protein crosslinking include aspartic acid, asparagine, cysteine, lysine, phosphoserine, phosphothreonine, glutamic acid and glutamine. It has become clear, therefore, that several amino acids can act as potential sites for crosslinking in the long-lived proteins that are present in aged individuals. Moreover, the lens has been an invaluable model tissue and source of crosslinked proteins from which to determine crosslinking mechanisms that may lead to crosslinking in other human tissues.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics

Recent studies have revealed diverse amino acid, post-translational, and noncanonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of substoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI's robust parametrization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).

Discovering the landscape of protein modifications

Protein modifications modulate nearly every aspect of cell biology in organisms, ranging from Archaea to Eukaryotes. The earliest evidence of covalent protein modifications was found in the early 20th century by studying the amino acid composition of proteins by chemical hydrolysis. These discoveries challenged what defined a canonical amino acid. The advent and rapid adoption of mass-spectrometry-based proteomics in the latter part of the 20th century enabled a veritable explosion in the number of known protein modifications, with more than 500 discrete modifications counted today. Now, new computational tools in data science, machine learning, and artificial intelligence are poised to allow researchers to make significant progress in discovering new protein modifications and determining their function. In this review, we take an opportunity to revisit the historical discovery of key post-translational modifications, quantify the current landscape of covalent protein adducts, and assess the role that new computational tools will play in the future of this field.

MutCombinator: identification of mutated peptides allowing combinatorial mutations using nucleotide-based graph search

Motivation

Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next-generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources.

Results

We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for non-coding regions.

Availability and implementation

https://prix.hanyang.ac.kr/download/mutcombinator.jsp.

Supplementary information

Supplementary data are available at Bioinformatics online.

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.

Open Search Strategy for Inferring the Masses of Cross-Link Adducts on Proteins

Development of new reagents for protein cross-linking is constantly ongoing. The chemical formulas for the linker adducts formed by these reagents are usually deduced from expert knowledge and then validated by mass spectrometry. Clearly, it would be more rigorous to infer the chemical compositions of the adducts directly from the data without any prior assumptions on their chemistries. Unfortunately, the analysis tools that are currently available to detect chemical modifications on linear peptides are not applicable to the case of two cross-linked peptides. Here, we show that an adaptation of the open search strategy that works on linear peptides can be used to characterize cross-link modifications in pairs of peptides. We benchmark our approach by correctly inferring the linker masses of two well-known reagents, DSS and formaldehyde, to accuracies of a few parts per million. We then investigate the cross-linking chemistries of two poorly characterized reagents: EMCS and glutaraldehyde. In the case of EMCS, we find that the expected cross-linking chemistry is accompanied by a competing chemistry that targets other amino acid types. In the case of glutaraldehyde, we find that the chemical formula of the dominant linker is C₅H₄, which indicates a ringed aromatic structure. These results demonstrate how, with very little effort, our approach can yield nontrivial insights to better characterize new cross-linkers.

PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results

Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of post-translational modification profiles detected in open searches based on attributes, such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multiexperiment comparisons for studying changes in modification profiles, e.g., in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed and paraffin-embedded samples, detects extreme underalkylation of cysteine in some data sets, discovers an artifactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multicenter proteomics profiling study.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

Profiling of post-translational modifications by chemical and computational proteomics

Post-translational modifications (PTMs) diversify the molecular structures of proteins and play essential roles in regulating their functions. Abnormal PTM status has been linked to a variety of developmental disorders and human diseases, highlighting the importance of studying PTMs in understanding physiological processes and discovering novel nodes and links with therapeutic intervention potential. Classical biochemical methods are suitable for studying PTMs on individual proteins; however, global profiling of PTMs in proteomes remains a challenging task. In this feature article, we start with a brief review of the traditional affinity-based strategies and shift the emphasis to summarizing recent progress in the development and application of chemical and computational proteomic strategies to delineate the global landscapes of functional PTMs. Finally, we discuss current challenges in PTM detection and provide future perspectives on how the field can be further advanced.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Identification of modified peptides using localization-aware open search

Identification of post-translationally or chemically modified peptides in mass spectrometry-based proteomics experiments is a crucial yet challenging task. We have recently introduced a fragment ion indexing method and the MSFragger search engine to empower an open search strategy for comprehensive analysis of modified peptides. However, this strategy does not consider fragment ions shifted by unknown modifications, preventing modification localization and limiting the sensitivity of the search. Here we present a localization-aware open search method, in which both modification-containing (shifted) and regular fragment ions are indexed and used in scoring. We also implement a fast mass calibration and optimization method, allowing optimization of the mass tolerances and other key search parameters. We demonstrate that MSFragger with mass calibration and localization-aware open search identifies modified peptides with significantly higher sensitivity and accuracy. Comparing MSFragger to other modification-focused tools (pFind3, MetaMorpheus, and TagGraph) shows that MSFragger remains an excellent option for fast, comprehensive, and sensitive searches for modified peptides in shotgun proteomics data.

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.

Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions

Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis.

Chemical RNA digestion enables robust RNA-binding site mapping at single amino acid resolution

RNA-binding sites (RBSs) can be identified by liquid chromatography and tandem mass spectrometry analyses of the protein-RNA conjugates created by crosslinking, but RBS mapping remains highly challenging due to the complexity of the formed RNA adducts. Here, we introduce RBS-ID, a method that uses hydrofluoride to fully cleave RNA into mono-nucleosides, thereby minimizing the search space to drastically enhance coverage and to reach single amino acid resolution. Moreover, the simple mono-nucleoside adducts offer a confident and quantitative measure of direct RNA-protein interaction. Using RBS-ID, we profiled ~2,000 human RBSs and probed Streptococcus pyogenes Cas9 to discover residues important for genome editing.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Redox Regulation via Glutaredoxin-1 and Protein S-Glutathionylation

Significance: Over the past several years, oxidative post-translational modifications of protein cysteines have been recognized for their critical roles in physiology and pathophysiology. Cells have harnessed thiol modifications involving both oxidative and reductive steps for signaling and protein processing. One of these stages requires oxidation of cysteine to sulfenic acid, followed by two reduction reactions. First, glutathione (reduced glutathione [GSH]) forms a S-glutathionylated protein, and second, enzymatic or chemical reduction removes the modification. Under physiological conditions, these steps confer redox signaling and protect cysteines from irreversible oxidation. However, oxidative stress can overwhelm protein S-glutathionylation and irreversibly modify cysteine residues, disrupting redox signaling. Critical Issues: Glutaredoxins mainly catalyze the removal of protein-bound GSH and help maintain protein thiols in a highly reduced state without exerting direct antioxidant properties. Conversely, glutathione S-transferase (GST), peroxiredoxins, and occasionally glutaredoxins can also catalyze protein S-glutathionylation, thus promoting a dynamic redox environment. Recent Advances: The latest studies of glutaredoxin-1 (Glrx) transgenic or knockout mice demonstrate important distinct roles of Glrx in a variety of pathologies. Endogenous Glrx is essential to maintain normal hepatic lipid homeostasis and prevent fatty liver disease. Further, in vivo deletion of Glrx protects lungs from inflammation and bacterial pneumonia-induced damage, attenuates angiotensin II-induced cardiovascular hypertrophy, and improves ischemic limb vascularization. Meanwhile, exogenous Glrx administration can reverse pathological lung fibrosis. Future Directions: Although S-glutathionylation modifies many proteins, these studies suggest that S-glutathionylation and Glrx regulate specific pathways in vivo, and they implicate Glrx as a potential novel therapeutic target to treat diverse disease conditions. Antioxid. Redox Signal. 32, 677-700.

Treasure hunt for peptides with undefined chemical modifications: Proteomics identification of differential albumin adducts of 2-nitroimidazole-indocyanine green in hypoxic tumor

2-Nitroimidazole is a well-known chemical probe targeting hypoxic environments of solid tumors, and its derivatives are widely used as imaging agents to investigate tissue and tumor hypoxia. However, the underlying chemistry for the hypoxia-detection capability of 2-nitroimidazole is still unclear. In this study, we deployed a biotin conjugate of 2-nitroimidazole-indocyanine green (2-nitro-ICG) for the investigation of in vivo hypoxia-probing mechanism of 2-nitro-ICG compounds. By implementing mass spectrometry-based proteomics and exhaustive data mining, we report that 2-nitro-ICG and its fragments modify mouse serum albumin as the primary protein target but at two structurally distinct sites and possibly via two different mechanisms. The identification of probe-modified peptides not only contributes to the understanding of the in vivo metabolism of 2-nitroimidazole compounds but also demonstrates a competent analytical workflow that enables the search for peptides with undefined modifications in complex proteome digests.

Shot-gun proteomics: why thousands of unidentified signals matter

Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.

Proteome-Wide Identification of Lysine Propionylation in the Conidial and Mycelial Stages of Trichophyton rubrum

Posttranslational modifications (PTMs) exist in a wide variety of organisms and play key roles in regulating various essential biological processes. Lysine propionylation is a newly discovered PTM that has rarely been identified in fungi. Trichophyton rubrum (T. rubrum) is one of the most common fungal pathogens in the world and has been studied as an important model organism of anthropic pathogenic filamentous fungi. In this study, we performed a proteome-wide propionylation analysis in the conidial and mycelial stages of T. rubrum. A total of 157 propionylated sites on 115 proteins were identified, and the high confidence of propionylation identification was validated by parallel reaction monitoring (PRM) assay. The results show that the propionylated proteins were mostly involved in various metabolic pathways. Histones and 15 pathogenicity-related proteins were also targets for propionylation modification, suggesting their roles in epigenetic regulation and pathogenicity. A comparison of the conidial and mycelial stages revealed that most propionylated proteins and sites were growth-stage specific and independent of protein abundance. Based on the function classifications, the propionylated proteins had a similar distribution in both stages; however, some differences were also identified. Furthermore, our results show that the concentration of propionyl-CoA had a significant influence on the propionylation level. In addition to the acetylation, succinylation and propionylation identified in T. rubrum, 26 other PTMs were also found to exist in this fungus. Overall, our study provides the first global propionylation profile of a pathogenic fungus. These results would be a foundation for further research on the regulation mechanism of propionylation in T. rubrum, which will enhance our understanding of the physiological features of T. rubrum and provide some clues for the exploration of improved therapies to treat this medically important fungus.

MODplus: Robust and Unrestrictive Identification of Post-Translational Modifications Using Mass Spectrometry

Post-translational modifications regulate various cellular processes and are of great biological interest. Unrestrictive searches of mass spectrometry data enable the detection of any type of modification. Here we propose MODplus, which makes practical unrestrictive searches possible by allowing (1) hundreds of modifications, (2) multiple modifications per peptide, (3) the whole proteome database, and (4) any tolerant values in search parameters. The utility of MODplus was demonstrated in large human data sets of HEK293 cells and TMT-labeled phosphorylation enrichment. Notably, MODplus supports identifying different modification types at multiple sites and reports real chemical and biological modifications, as it has been very labor intensive to link unrestrictive search results to real modifications. We also confirmed the presence of Missing Precursor (MP) spectra that were not identifiable using targeted precursor masses. The MP spectra mostly resulted in identifications of wrong modifications and negatively affected the overall performance, often by as much as 10%. MODplus can rapidly recognize MP spectra and correct their identifications, resulting in increased identification rate up to 70% in the HEK293 data set as well as improved reliability.

Regulation of phosphoribosyl ubiquitination by a calmodulin-dependent glutamylase

The bacterial pathogen Legionella pneumophila creates an intracellular niche permissive for its replication by extensively modulating host-cell functions using hundreds of effector proteins delivered by its Dot/Icm secretion system¹. Among these, members of the SidE family (SidEs) regulate several cellular processes through a unique phosphoribosyl ubiquitination mechanism that bypasses the canonical ubiquitination machinery^2-4. The activity of SidEs is regulated by another Dot/Icm effector known as SidJ⁵; however, the mechanism of this regulation is not completely understood^6,7. Here we demonstrate that SidJ inhibits the activity of SidEs by inducing the covalent attachment of glutamate moieties to SdeA-a member of the SidE family-at E860, one of the catalytic residues that is required for the mono-ADP-ribosyltransferase activity involved in ubiquitin activation². This inhibition by SidJ is spatially restricted in host cells because its activity requires the eukaryote-specific protein calmodulin (CaM). We solved a structure of SidJ-CaM in complex with AMP and found that the ATP used in this reaction is cleaved at the α-phosphate position by SidJ, which-in the absence of glutamate or modifiable SdeA-undergoes self-AMPylation. Our results reveal a mechanism of regulation in bacterial pathogenicity in which a glutamylation reaction that inhibits the activity of virulence factors is activated by host-factor-dependent acyl-adenylation.