Enhanced Global Post-translational Modification Discovery with MetaMorpheus

Identification of Host Proteins Involved in Hepatitis B Virus Genome Packaging

A critical part of the hepatitis B virus (HBV) life cycle is the packaging of the pregenomic RNA (pgRNA) into nucleocapsids. While this process is known to involve several viral elements, much less is known about the identities and roles of host proteins in this process. To better understand the role of host proteins, we isolated pgRNA and characterized its protein interactome in cells expressing either packaging-competent or packaging-incompetent HBV genomes. We identified over 250 host proteins preferentially associated with pgRNA from the packaging-competent version of the virus. These included proteins already known to support capsid formation, enhance viral gene expression, catalyze nucleocapsid dephosphorylation, and bind to the viral genome, demonstrating the ability of the approach to effectively reveal functionally significant host-virus interactors. Three of these host proteins, AURKA, YTHDF2, and ATR, were selected for follow-up analysis. RNA immunoprecipitation qPCR (RIP-qPCR) confirmed pgRNA-protein association in cells, and siRNA knockdown of the proteins showed decreased encapsidation efficiency. This study provides a template for the use of comparative RNA-protein interactome analysis in conjunction with virus engineering to reveal functionally significant host-virus interactions.

Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain

Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.

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.

TermineR: Extracting information on endogenous proteolytic processing from shotgun proteomics data

State-of-the-art mass spectrometers combined with modern bioinformatics algorithms for peptide-to-spectrum matching (PSM) with robust statistical scoring allow for more variable features (i.e., post-translational modifications) being reliably identified from (tandem-) mass spectrometry data, often without the need for biochemical enrichment. Semi-specific proteome searches, that enforce a theoretical enzymatic digestion to solely the N- or C-terminal end, allow to identify of native protein termini or those arising from endogenous proteolytic activity (also referred to as "neo-N-termini" analysis or "N-terminomics"). Nevertheless, deriving biological meaning from these search outputs can be challenging in terms of data mining and analysis. Thus, we introduce TermineR, a data analysis approach for the (1) annotation of peptides according to their enzymatic cleavage specificity and known protein processing features, (2) differential abundance and enrichment analysis of N-terminal sequence patterns, and (3) visualization of neo-N-termini location. We illustrate the use of TermineR by applying it to tandem mass tag (TMT)-based proteomics data of a mouse model of polycystic kidney disease, and assess the semi-specific searches for biological interpretation of cleavage events and the variable contribution of proteolytic products to general protein abundance. The TermineR approach and example data are available as an R package at https://github.com/MiguelCos/TermineR.

Recent Contributions of Proteomics to Our Understanding of Reversible Nε-Lysine Acylation in Bacteria

Post-translational modifications (PTMs) have been extensively studied in both eukaryotes and prokaryotes. Lysine acetylation, originally thought to be a rare occurrence in bacteria, is now recognized as a prevalent and important PTM in more than 50 species. This expansion in interest in bacterial PTMs became possible with the advancement of mass spectrometry technology and improved reagents such as acyl-modification specific antibodies. In this Review, we discuss how mass spectrometry-based proteomic studies of lysine acetylation and other acyl modifications have contributed to our understanding of bacterial physiology, focusing on recently published studies from 2018 to 2023. We begin with a discussion of approaches used to study bacterial PTMs. Next, we discuss newly characterized acylomes, including acetylomes, succinylomes, and malonylomes, in different bacterial species. In addition, we examine proteomic contributions to our understanding of bacterial virulence and biofilm formation. Finally, we discuss the contributions of mass spectrometry to our understanding of the mechanisms of acetylation, both enzymatic and nonenzymatic. We end with a discussion of the current state of the field and possible future research avenues to explore.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications

Proteoforms are all forms of protein molecules from the same gene because of variations at the DNA, RNA, and protein levels, e.g., alternative splicing and post-translational modifications (PTMs). Delineation of proteins in a proteoform-specific manner is crucial for understanding their biological functions. Mass spectrometry (MS)-intensive top-down proteomics (TDP) is promising for comprehensively characterizing intact proteoforms in complex biological systems. It has achieved substantial progress in technological development, including sample preparation, proteoform separations, MS instrumentation, and bioinformatics tools. In a single TDP study, thousands of proteoforms can be identified and quantified from a cell lysate. It has also been applied to various biomedical research to better our understanding of protein function in regulating cellular processes and to discover novel proteoform biomarkers of diseases for early diagnosis and therapeutic development. This review covers the most recent technological development and biomedical applications of MS-intensive TDP.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

The annotation of GBA1 has been concealed by its protein-coding pseudogene GBAP1

Mutations in GBA1 cause Gaucher disease and are the most important genetic risk factor for Parkinson's disease. However, analysis of transcription at this locus is complicated by its highly homologous pseudogene, GBAP1. We show that >50% of short RNA-sequencing reads mapping to GBA1 also map to GBAP1. Thus, we used long-read RNA sequencing in the human brain, which allowed us to accurately quantify expression from both GBA1 and GBAP1. We discovered significant differences in expression compared to short-read data and identify currently unannotated transcripts of both GBA1 and GBAP1. These included protein-coding transcripts from both genes that were translated in human brain, but without the known lysosomal function-yet accounting for almost a third of transcription. Analyzing brain-specific cell types using long-read and single-nucleus RNA sequencing revealed region-specific variations in transcript expression. Overall, these findings suggest nonlysosomal roles for GBA1 and GBAP1 with implications for our understanding of the role of GBA1 in health and disease.

Transcriptomic analysis of Bombyx mori in its early larval stage (2nd instar) of development upon Nosema bombycis transovarial infection

The infection caused by Nosema bombycis often known as pebrine, is a devastating sericulture disease. The infection can be transmitted to the next generation through eggs laid by infected female Bombyx mori moths (transovarial) as well as with N. bombycis contaminated food (horizontal). Most diagnoses were carried out in the advanced stages of infection until the time that infection might spread to other healthy insects. Hence, early diagnosis of pebrine is of utmost importance to quarantine infected larvae from uninfected silkworm batches and stop further spread of the infection. The findings of our study provide an insight into how the silkworm larval host defence system was activated against early N. bombycis transovarial infection. The results obtained from transcriptome analysis of infected 2nd instar larvae revealed significant (adjusted P-value < 0.05) expression of 1888 genes of which 801 genes were found to be upregulated and 1087 genes were downregulated when compared with the control. Pathway analysis indicated activation of the immune deficiency (IMD) pathway, which shows a potential immune defence response against pebrine infection as well as suppression of the melanin synthesis pathway due to lower expression of prophenoloxidase activating enzyme (PPAE). Liquid chromatography mass spectrometry (LC-MS/MS) analysis of haemolymph from infected larvae shows the secretion of serpin binding protein of N. bombycis which might be involved in the suppression of the melanization pathway. Moreover, among the differentially expressed genes, we found that LPMC-61, yellow-y, gasp and osiris 9 can be utilised as potential markers for early diagnosis of transovarial pebrine infection in B. mori. Physiological as well as biochemical roles and functions of many of the essential genes are yet to be established, and enlightened research will be required to characterize the products of these genes.

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.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

IsoBayes: a Bayesian approach for single-isoform proteomics inference

Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Panda-UV Unlocks Deeper Protein Characterization with Internal Fragments in Ultraviolet Photodissociation Mass Spectrometry

Ultraviolet photodissociation (UVPD) mass spectrometry unlocks insights into the protein structure and sequence through fragmentation patterns. While N- and C-terminal fragments are traditionally relied upon, this work highlights the critical role of internal fragments in achieving near-complete sequencing of protein. Previous limitations of internal fragment utilization, owing to their abundance and potential for random matching, are addressed here with the development of Panda-UV, a novel software tool combining spectral calibration, and Pearson correlation coefficient scoring for confident fragment assignment. Panda-UV showcases its power through comprehensive benchmarks on three model proteins. The inclusion of internal fragments boosts identified fragment numbers by 26% and enhances average protein sequence coverage to a remarkable 93% for intact proteins, unlocking the hidden region of the largest protein carbonic anhydrase II in model proteins. Notably, an average of 65% of internal fragments can be identified in multiple replicates, demonstrating the high confidence of the fragments Panda-UV provided. Finally, the sequence coverages of mAb subunits can be increased up to 86% and the complementary determining regions (CDRs) are nearly completely sequenced in a single experiment. The source codes of Panda-UV are available at https://github.com/PHOENIXcenter/Panda-UV.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

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.

Spectral averaging with outlier rejection algorithms to increase identifications in top-down proteomics

The identification of proteoforms by top-down proteomics requires both high quality fragmentation spectra and the neutral mass of the proteoform from which the fragments derive. Intact proteoform spectra can be highly complex and may include multiple overlapping proteoforms, as well as many isotopic peaks and charge states. The resulting lower signal-to-noise ratios for intact proteins complicates downstream analyses such as deconvolution. Averaging multiple scans is a common way to improve signal-to-noise, but mass spectrometry data contains artifacts unique to it that can degrade the quality of an averaged spectra. To overcome these limitations and increase signal-to-noise, we have implemented outlier rejection algorithms to remove outlier measurements efficiently and robustly in a set of MS1 scans prior to averaging. We have implemented averaging with rejection algorithms in the open-source, freely available, proteomics search engine MetaMorpheus. Herein, we report the application of the averaging with rejection algorithms to direct injection and online liquid chromatography mass spectrometry data. Averaging with rejection algorithms demonstrated a 45% increase in the number of proteoforms detected in Jurkat T cell lysate. We show that the increase is due to improved spectral quality, particularly in regions surrounding isotopic envelopes.

Loss-of-function cancer-linked mutations in the EIF4G2 non-canonical translation initiation factor

Tumor cells often exploit the protein translation machinery, resulting in enhanced protein expression essential for tumor growth. Since canonical translation initiation is often suppressed because of cell stress in the tumor microenvironment, non-canonical translation initiation mechanisms become particularly important for shaping the tumor proteome. EIF4G2 is a non-canonical translation initiation factor that mediates internal ribosome entry site (IRES)- and uORF-dependent initiation mechanisms, which can be used to modulate protein expression in cancer. Here, we explored the contribution of EIF4G2 to cancer by screening the COSMIC database for EIF4G2 somatic mutations in cancer patients. Functional examination of missense mutations revealed deleterious effects on EIF4G2 protein-protein interactions and, importantly, on its ability to mediate non-canonical translation initiation. Specifically, one mutation, R178Q, led to reductions in protein expression and near-complete loss of function. Two other mutations within the MIF4G domain specifically affected EIF4G2's ability to mediate IRES-dependent translation initiation but not that of target mRNAs with uORFs. These results shed light on both the structure-function of EIF4G2 and its potential tumor suppressor effects.

The need for open and FAIR data in top-down proteomics

In recent years, there has been a tremendous evolution in the high-throughput, tandem mass spectrometry-based analysis of intact proteins, also known as top-down proteomics (TDP). Both hardware and software have developed to the point that the technique has largely entered the mainstream, and large-scale, ambitious, multi-laboratory initiatives have started to make their appearance in the literature. For this, however, more convenient and robust data sharing and reuse will be required. Walzer et al. have created TopDownApp, a customisable, open platform for visualisation and analysis of TDP data, which they hope will be a step in this direction. As they point out, other benefits of such data sharing and interoperability would include reanalysis of published datasets, as well as the prospect of using large amounts of data to train machine learning algorithms. In time, this work could prove to be a valuable resource in the move towards a future of greater TDP data findability, accessibility, interoperability and reusability.

Defining Distinct RNA-Protein Interactomes of SARS-CoV-2 Genomic and Subgenomic RNAs

Host RNA binding proteins recognize viral RNA and play key roles in virus replication and antiviral mechanisms. SARS-CoV-2 generates a series of tiered subgenomic RNAs (sgRNAs), each encoding distinct viral protein(s) that regulate different aspects of viral replication. Here, for the first time, we demonstrate the successful isolation of SARS-CoV-2 genomic RNA and three distinct sgRNAs (N, S, and ORF8) from a single population of infected cells and characterize their protein interactomes. Over 500 protein interactors (including 260 previously unknown) were identified as associated with one or more target RNA. These included protein interactors unique to a single RNA pool and others present in multiple pools, highlighting our ability to discriminate between distinct viral RNA interactomes despite high sequence similarity. Individual interactomes indicated viral associations with cell response pathways, including regulation of cytoplasmic ribonucleoprotein granules and posttranscriptional gene silencing. We tested the significance of three protein interactors in these pathways (APOBEC3F, PPP1CC, and MSI2) using siRNA knockdowns, with several knockdowns affecting viral gene expression, most consistently PPP1CC. This study describes a new technology for high-resolution studies of SARS-CoV-2 RNA regulation and reveals a wealth of new viral RNA-associated host factors of potential functional significance to infection.

A proteomic perspective on the resistance response of Klebsiella pneumoniae to antimicrobial peptide PaDBS1R1

BACKGROUND

The synthetic antimicrobial peptide, PaDBS1R1, has been reported as a powerful anti-Klebsiella pneumoniae antimicrobial. However, there is only scarce knowledge about whether K. pneumoniae could develop resistance against PaDBS1R1 and which resistance mechanisms could be involved.

OBJECTIVES

Identify via label-free shotgun proteomics the K. pneumoniae resistance mechanisms developed against PaDBS1R1.

METHODS

An adaptive laboratory evolution experiment was performed to obtain a PaDBS1R1-resistant K. pneumoniae lineage. Antimicrobial susceptibility was determined through microdilution assay. Modifications in protein abundances between the resistant and sensitive lineages were measured via label-free quantitative shotgun proteomics. Enriched Gene Ontology terms and KEGG pathways were identified through over-representation analysis. Data are available via ProteomeXchange with identifier PXD033020.

RESULTS

K. pneumoniae ATCC 13883 parental strain challenged with increased subinhibitory PaDBS1R1 concentrations allowed the PaDBS1R1-resistant K. pneumoniae lineage to emerge. Proteome comparisons between PaDBS1R1-resistant K. pneumoniae and PaDBS1R1-sensitive K. pneumoniae under PaDBS1R1-induced stress conditions enabled the identification and quantification of 1702 proteins, out of which 201 were differentially abundant proteins (DAPs). The profiled DAPs comprised 103 up-regulated proteins (adjusted P value < 0.05, fold change ≥ 2) and 98 down-regulated proteins (adjusted P value < 0.05, fold change ≤ 0.5). The enrichment analysis suggests that PhoPQ-guided LPS modifications and CpxRA-dependent folding machinery could be relevant resistance mechanisms against PaDBS1R1.

CONCLUSIONS

Based on experimental evolution and a label-free quantitative shotgun proteomic approach, we showed that K. pneumoniae developed resistance against PaDBS1R1, whereas PhoPQ-guided LPS modifications and CpxRA-dependent folding machinery appear to be relevant resistance mechanisms against PaDBS1R1.

Bioinformatic Workflows for Metaproteomics

The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.

Analyzing Posttranslational Modifications in Single Cells

With the rapid expansion of capabilities in the analysis of proteins in single cells, we can now identify multiple classes of protein posttranslational modifications on some of these proteins. Each new technology that has increased the number of proteins measured per cell has likewise increased our ability to identify and quantify modified peptides. In this chapter, I will discuss our current capabilities, concerns, and challenges specific to this emerging field of study and the inevitable demand for services, providing a general review of concepts that should be considered.

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.

Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from multiple complex modifications of peptides

BACKGROUND

In proteomics, the interpretation of mass spectra representing peptides carrying multiple complex modifications remains challenging, as it is difficult to strike a balance between reasonable execution time, a limited number of false positives, and a huge search space allowing any number of modifications without a priori. The scientific community needs new developments in this area to aid in the discovery of novel post-translational modifications that may play important roles in disease.

RESULTS

To make progress on this issue, we implemented SpecGlobX (SpecGlob eXTended to eXperimental spectra), a standalone Java application that quickly determines the best spectral alignments of a (possibly very large) list of Peptide-to-Spectrum Matches (PSMs) provided by any open modification search method, or generated by the user. As input, SpecGlobX reads a file containing spectra in MGF or mzML format and a semicolon-delimited spreadsheet describing the PSMs. SpecGlobX returns the best alignment for each PSM as output, splitting the mass difference between the spectrum and the peptide into one or more shifts while considering the possibility of non-aligned masses (a phenomenon resulting from many situations including neutral losses). SpecGlobX is fast, able to align one million PSMs in about 1.5 min on a standard desktop. Firstly, we remind the foundations of the algorithm and detail how we adapted SpecGlob (the method we previously developed following the same aim, but limited to the interpretation of perfect simulated spectra) to the interpretation of imperfect experimental spectra. Then, we highlight the interest of SpecGlobX as a complementary tool downstream to three open modification search methods on a large simulated spectra dataset. Finally, we ran SpecGlobX on a proteome-wide dataset downloaded from PRIDE to demonstrate that SpecGlobX functions just as well on simulated and experimental spectra. We then carefully analyzed a limited set of interpretations.

CONCLUSIONS

SpecGlobX is helpful as a decision support tool, providing keys to interpret peptides carrying complex modifications still poorly considered by current open modification search software. Better alignment of PSMs enhances confidence in the identification of spectra provided by open modification search methods and should improve the interpretation rate of spectra.

Characterization of Proteoform Post-Translational Modifications by Top-Down and Bottom-Up Mass Spectrometry in Conjunction with Annotations

Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledge bases, such as UniProt, provide valuable information for PTM characterization and verification. Here, we present a software pipeline PTM-TBA (PTM characterization by Top-down and Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as PTM annotations. We assessed PTM-TBA using a technical triplicate of bottom-up and top-down MS data of SW480 cells. On average, database search of the top-down MS data identified 2000 mass shifts, 814.5 (40.7%) of which were matched to 11 common PTMs and 423 of which were localized. Of the mass shifts identified by top-down MS, PTM-TBA verified 435 mass shifts using the bottom-up MS data and UniProt annotations.

ReCom: A semi-supervised approach to ultra-tolerant database search for improved identification of modified peptides

Open-search methods allow unbiased, high-throughput identification of post-translational modifications in proteins at an unprecedented scale. The performance of current open-search algorithms is diminished by experimental errors in the determination of the precursor peptide mass. In this work we propose a semi-supervised open search approach, called ReCom, that minimizes this effect by taking advantage of a priori known information from a reference database, such as Unimod or a database provided by the user. We present a proof-of-concept study using Comet-ReCom, an improved version of Comet-PTM. Comet-ReCom increased identification performance of Comet-PTM by 68%. This increased performance of Comet-ReCom to score the MS/MS spectrum comes in parallel with a significantly better assignation of the monoisotopic peak of the precursor peptide in the MS spectrum, even in cases of peptide coelution. Our data demonstrate that open searches using ultra-tolerant mass windows can benefit from using a semi-supervised approach that takes advantage from previous knowledge on the nature of protein modifications. SIGNIFICANCE: The present study introduces a novel approach to ultra-tolerant database search, which employs prior knowledge of post-translational modifications (PTMs) to improve identification of modified peptides. This method addresses the limitations related to experimental errors and precursor mass assignation of previous open-search methods. Thus, it enables the study of the biological significance of a wider variety of PTMs, including unknown or unexpected modifications that may have gone unnoticed using non-supervised search methods.

Querying the In Vitro Proteome Cysteine Reactivity of 8:2 Fluorotelomer Acrylate

Despite the phase out of legacy per- and polyfluoroalkyl substances (PFAS), fluorotelomer-based polymers (FTP) have been used for many applications, notably textile surface coatings. FTPs are of a health concern due to their breakdown into legacy PFAS and the co-occurrence of fluorotelomer acrylate (FTAC) monomers, of which the latter may potentially react with cellular thiols. To evaluate this hypothesis, we employed fluorous-solid-phase extraction (FSPE), to enrich peptides covalently modified by 8:2 fluorotelomer acrylate (8:2 FTAC) and coupled it to a modified nano-liquid chromatography method for the identification of in vitro protein adducts using bottom-up data-dependent proteomics analysis. Using this method, over 100 unique peptides were detected with 8:2 FTAC modifications, although none of the modified cysteine residues were annotated active site nucleophiles. In parallel, a synthetic C6F13-iodoacetamide (F13-IAM) chemical probe was used to gauge the upper bound of PFAS-thiol reactivity. Over seven hundred peptides were detected with modifications but only 9 of 28 annotated active site cysteines in this dataset were modified by F13-IAM. Further exploration of the impacts of 8:2 FTAC adducts on protein function revealed that 8:2 FTAC modification promotes protein aggregation in vitro. These results suggest that 8:2 FTAC may exhibit significant proteome thiol reactivity and imply a more general mechanism of toxicity of PFAS-induced protein aggregation.

Microglia mediate contact-independent neuronal pruning via secreted Neuraminidase-3 associated with extracellular vesicles

Neurons communicate with each other through electrochemical transmission at synapses. Microglia, the resident immune cells of the central nervous system, can prune these synapses through a variety of contact-dependent and -independent means. Microglial secretion of active sialidase enzymes upon exposure to inflammatory stimuli is one unexplored mechanism of pruning. Recent work from our lab showed that treatment of neurons with bacterial sialidases disrupts neuronal network connectivity. Here, we find that activated microglia secrete Neuraminidase-3 (Neu3) associated with fusogenic extracellular vesicles. Furthermore, we show Neu3 mediates contact-independent pruning of neurons and subsequent disruption of neuronal networks through neuronal glycocalyx remodeling. We observe that NEU3 is transcriptionally upregulated upon exposure to inflammatory stimuli, and that a genetic knock-out of NEU3 abrogates the sialidase activity of inflammatory microglial secretions. Moreover, we demonstrate that Neu3 is associated with a subpopulation of extracellular vesicles, possibly exosomes, that are secreted by microglia upon inflammatory insult. Finally, we demonstrate that Neu3 is both necessary and sufficient to both desialylate neurons and decrease neuronal network connectivity. These results implicate Neu3 in remodeling of the glycocalyx leading to aberrant network-level activity of neurons, with implications in neuroinflammatory diseases such as Parkinson's disease and Alzheimer's disease.

Integrative Proteogenomics Using ProteomeGenerator2

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and protein sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as noncanonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

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.

Newly synthesized glycoprotein profiling to identify molecular signatures of warm ischemic injury in donor lungs

Despite recent technological advances such as ex vivo lung perfusion (EVLP), the outcome of lung transplantation remains unsatisfactory with ischemic injury being a common cause for primary graft dysfunction. New therapeutic developments are hampered by limited understanding of pathogenic mediators of ischemic injury to donor lung grafts. Here, to identify novel proteomic effectors underlying the development of lung graft dysfunction, using bioorthogonal protein engineering, we selectively captured and identified newly synthesized glycoproteins (NewS-glycoproteins) produced during EVLP with unprecedented temporal resolution of 4 h. Comparing the NewS-glycoproteomes in lungs with and without warm ischemic injury, we discovered highly specific proteomic signatures with altered synthesis in ischemic lungs, which exhibited close association to hypoxia response pathways. Inspired by the discovered protein signatures, pharmacological modulation of the calcineurin pathway during EVLP of ischemic lungs offered graft protection and improved posttransplantation outcome. In summary, the described EVLP-NewS-glycoproteomics strategy delivers an effective new means to reveal molecular mediators of donor lung pathophysiology and offers the potential to guide future therapeutic development.NEW & NOTEWORTHY This study developed and implemented a bioorthogonal strategy to chemoselectively label, enrich, and characterize newly synthesized (NewS-)glycoproteins during 4-h ex vivo lung perfusion (EVLP). Through this approach, the investigators uncovered specific proteomic signatures associated with warm ischemic injury in donor lung grafts. These signatures exhibit high biological relevance to ischemia-reperfusion injury, validating the robustness of the presented approach.

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

Defining distinct RNA-protein interactomes of SARS-CoV-2 genomic and subgenomic RNAs

Host RNA binding proteins recognize viral RNA and play key roles in virus replication and antiviral defense mechanisms. SARS-CoV-2 generates a series of tiered subgenomic RNAs (sgRNAs), each encoding distinct viral protein(s) that regulate different aspects of viral replication. Here, for the first time, we demonstrate the successful isolation of SARS-CoV-2 genomic RNA and three distinct sgRNAs (N, S, and ORF8) from a single population of infected cells and characterize their protein interactomes. Over 500 protein interactors (including 260 previously unknown) were identified as associated with one or more target RNA at either of two time points. These included protein interactors unique to a single RNA pool and others present in multiple pools, highlighting our ability to discriminate between distinct viral RNA interactomes despite high sequence similarity. The interactomes indicated viral associations with cell response pathways including regulation of cytoplasmic ribonucleoprotein granules and posttranscriptional gene silencing. We validated the significance of five protein interactors predicted to exhibit antiviral activity (APOBEC3F, TRIM71, PPP1CC, LIN28B, and MSI2) using siRNA knockdowns, with each knockdown yielding increases in viral production. This study describes new technology for studying SARS-CoV-2 and reveals a wealth of new viral RNA-associated host factors of potential functional significance to infection.

Elucidating the RNA-Protein Interactomes of Target RNAs in Tissue

RNA-protein interactions are key to many aspects of cellular homeostasis and their identification is important to understanding cellular function. Multiple strategies have been developed for the RNA-centric characterization of RNA-protein complexes. However, these studies have all been done in immortalized cell lines that do not capture the complexity of heterogeneous tissue samples. Here, we develop hybridization purification of RNA-protein complexes followed by mass spectrometry (HyPR-MS) for use in tissue samples. We isolated both polyadenylated RNA and the specific long noncoding RNA MALAT1 and characterized their protein interactomes. These results demonstrate the feasibility of HyPR-MS in tissue for the multiplexed characterization of specific RNA-protein complexes.

STAT5b is a key effector of NRG-1/ERBB4-mediated myocardial growth

The growth factor Neuregulin-1 (NRG-1) regulates myocardial growth and is currently under clinical investigation as a treatment for heart failure. Here, we demonstrate in several in vitro and in vivo models that STAT5b mediates NRG-1/EBBB4-stimulated cardiomyocyte growth. Genetic and chemical disruption of the NRG-1/ERBB4 pathway reduces STAT5b activation and transcription of STAT5b target genes Igf1, Myc, and Cdkn1a in murine cardiomyocytes. Loss of Stat5b also ablates NRG-1-induced cardiomyocyte hypertrophy. Dynamin-2 is shown to control the cell surface localization of ERBB4 and chemical inhibition of Dynamin-2 downregulates STAT5b activation and cardiomyocyte hypertrophy. In zebrafish embryos, Stat5 is activated during NRG-1-induced hyperplastic myocardial growth, and chemical inhibition of the Nrg-1/Erbb4 pathway or Dynamin-2 leads to loss of myocardial growth and Stat5 activation. Moreover, CRISPR/Cas9-mediated knockdown of stat5b results in reduced myocardial growth and cardiac function. Finally, the NRG-1/ERBB4/STAT5b signaling pathway is differentially regulated at mRNA and protein levels in the myocardium of patients with pathological cardiac hypertrophy as compared to control human subjects, consistent with a role of the NRG-1/ERBB4/STAT5b pathway in myocardial growth.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

Spatial analysis of the ancient proteome of archeological teeth using mass spectrometry imaging

RATIONALE

Proteins extracted from archaeological bone and teeth are utilised for investigating the phylogeny of extinct and extant species, the biological sex and age of past individuals, as well as ancient health and physiology. However, variable preservation of proteins in archaeological materials represents a major challenge.

METHODS

To better understand the spatial distribution of ancient proteins preserved within teeth, we applied matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) for the first time to bioarchaeological samples to visualise the intensity of proteins in archaeological teeth thin sections. We specifically explored the spatial distribution of four proteins (collagen type I, of which the chains alpha-1 and alpha-2, alpha-2-HS-glycoprotein, haemoglobin subunit alpha and myosin light polypeptide 6).

RESULTS

We successfully identified ancient proteins in archaeological teeth thin sections using mass spectrometry imaging. The data are available via ProteomeXchange with identifier PXD038114. However, we observed that peptides did not always follow our hypotheses for their spatial distribution, with distinct differences observed in the spatial distribution of several proteins, and occasionally between peptides of the same protein.

CONCLUSIONS

While it remains unclear what causes these differences in protein intensity distribution within teeth, as revealed by MALDI-MSI in this study, we have demonstrated that MALDI-MSI can be successfully applied to mineralised bioarchaeological tissues to detect ancient peptides. In future applications, this technique could be particularly fruitful not just for understanding the preservation of proteins in a range of archaeological materials, but making informed decisions on sampling strategies and the targeting of key proteins of archaeological and biological interest.

Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology

Conventional Environmental Risk Assessment (ERA) of pesticide pollution is based on soil concentrations and apical endpoints, such as the reproduction of test organisms, but has traditionally disregarded information along the organismal response cascade leading to an adverse outcome. The Adverse Outcome Pathway (AOP) framework includes response information at any level of biological organization, providing opportunities to use intermediate responses as a predictive read-out for adverse outcomes instead. Transcriptomic and proteomic data can provide thousands of data points on the response to toxic exposure. Combining multiple omics data types is necessary for a comprehensive overview of the response cascade and, therefore, AOP development. However, it is unclear if transcript and protein responses are synchronized in time or time lagged. To understand if analysis of multi-omics data obtained at the same timepoint reveal one synchronized response cascade, we studied time-resolved shifts in gene transcript and protein abundance in the springtail Folsomia candida, a soil ecotoxicological model, after exposure to the neonicotinoid insecticide imidacloprid. We analyzed transcriptome and proteome data every 12 h up to 72 h after onset of exposure. The most pronounced shift in both transcript and protein abundances was observed after 48 h exposure. Moreover, cross-correlation analyses indicate that most genes displayed the highest correlation between transcript and protein abundances without a time-lag. This demonstrates that a combined analysis of transcriptomic and proteomic data from the same time-point can be used for AOP improvement. This data will promote the development of biomarkers for the presence of neonicotinoid insecticides or chemicals with a similar mechanism of action in soils.

Axon guidance genes modulate neurotoxicity of ALS-associated UBQLN2

Mutations in the ubiquitin (Ub) chaperone Ubiquilin 2 (UBQLN2) cause X-linked forms of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) through unknown mechanisms. Here, we show that aggregation-prone, ALS-associated mutants of UBQLN2 (UBQLN2ALS) trigger heat stress-dependent neurodegeneration in Drosophila. A genetic modifier screen implicated endolysosomal and axon guidance genes, including the netrin receptor, Unc-5, as key modulators of UBQLN2 toxicity. Reduced gene dosage of Unc-5 or its coreceptor Dcc/frazzled diminished neurodegenerative phenotypes, including motor dysfunction, neuromuscular junction defects, and shortened lifespan, in flies expressing UBQLN2ALS alleles. Induced pluripotent stem cells (iPSCs) harboring UBQLN2ALS knockin mutations exhibited lysosomal defects while inducible motor neurons (iMNs) expressing UBQLN2ALS alleles exhibited cytosolic UBQLN2 inclusions, reduced neurite complexity, and growth cone defects that were partially reversed by silencing of UNC5B and DCC. The combined findings suggest that altered growth cone dynamics are a conserved pathomechanism in UBQLN2-associated ALS/FTD.

Modeling Lower-Order Statistics to Enable Decoy-Free FDR Estimation in Proteomics

One of the chief objectives in mass spectrometry-based peptide identification in proteomics is the statistical validation of top-scoring peptide-spectrum matches (PSMs) in the form of false discovery rate (FDR) estimation. Existing methods construct a null model that captures the characteristics of incorrect target PSMs to estimate the FDR, most often with the help of decoys. Decoy-based methods, however, increase the computational cost and rely on the difficult-to-verify assumption that decoy PSMs constitute a sufficient and representative sample of the population of possible incorrect target PSMs. On the other hand, the possibility of FDR estimation assisted by the plentiful non-top-scoring PSMs, which are almost always incorrect, has been scarcely explored. In this work, we propose a novel decoy-free procedure for developing null models for top-scoring PSMs using the transformed e-value (TEV) score and the distributions of non-top-scoring target PSMs. The method relies on a theoretically derivable relationship between the parameters of the distributions of lower-order statistics of the TEV score and a necessary empirical optimization to fit a single parameter to actual data. The framework was tested on multiple different data sets and two search engines. We present evidence that our method is comparable to and occasionally outperforms popular decoy-free and decoy-based methods in FDR estimation.

High-resolution mass spectrometry for glycoproteomics

Tweetable abstract Bottom-up glycoproteomics combined with top-down strategy allows direct analysis of glycoform-mapped glycosylation and its glycans by high-resolution mass spectrometry.

Challenges and opportunities for single cell computational proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it's necessary to realize that current algorithms being employed on single-cell data were designed for bulk data, and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data, and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

JUMPptm: Integrated software for sensitive identification of post-translational modifications and its application in Alzheimer's disease study

BACKGROUND

Mass spectrometry (MS)-based proteomic analysis of posttranslational modifications (PTMs) usually requires the pre-enrichment of modified proteins or peptides. However, recent ultra-deep whole proteome profiling generates millions of spectra in a single experiment, leaving many unassigned spectra, some of which may be derived from PTM peptides.

METHODS

Here we present JUMPptm, an integrative computational pipeline, to extract PTMs from unenriched whole proteome. JUMPptm combines the advantages of JUMP, MSFragger and Comet search engines, and includes de novo tags, customized database search and peptide filtering, which iteratively analyzes each PTM by a multi-stage strategy to improve sensitivity and specificity.

RESULTS

We applied JUMPptm to the deep brain proteome of Alzheimer's disease (AD), and identified 34,954 unique peptides with phosphorylation, methylation, acetylation, ubiquitination, and others. The phosphorylated peptides were validated by enriched phosphoproteome from the same sample. TMT-based quantification revealed 482 PTM peptides dysregulated at different stages during AD progression. For example, the acetylation of numerous mitochondrial proteins is significantly decreased in AD. A total of 60 PTM sites are found in the pan-PTM map of the Tau protein.

CONCLUSION

The JUMPptm program is an effective tool for pan-PTM analysis and the resulting AD pan-PTM profile serves as a valuable resource for AD research.

Simultaneous N-Deglycosylation and Digestion of Complex Samples on S-Traps Enables Efficient Glycosite Hypothesis Generation

N-linked glycosylation is an important post-translational modification that is difficult to identify and quantify in traditional bottom-up proteomics experiments. Enzymatic deglycosylation of proteins by peptide:N-glycosidase F (PNGase F) prior to digestion and subsequent mass spectrometry analysis has been shown to improve coverage of various N-linked glycopeptides, but the inclusion of this step may add up to a day to an already lengthy sample preparation process. An efficient way to integrate deglycosylation with bottom-up proteomics would be a valuable contribution to the glycoproteomics field. Here, we demonstrate a proteomics workflow in which deglycosylation and proteolytic digestion of samples occur simultaneously using suspension trapping (S-Trap). This approach adds no time to standard digestion protocols. Applying this sample preparation strategy to a human serum sample, we demonstrate improved identification of potential N-glycosylated peptides in deglycosylated samples compared with non-deglycosylated samples, identifying 156 unique peptides that contain the N-glycosylation motif (asparagine-X-serine/threonine), the deamidation modification characteristic of PNGase F, and an increase in peptide intensity over a control sample. We expect that this rapid sample preparation strategy will assist in the identification and quantification of both known and potential glycoproteins. Data are available via ProteomeXchange with the identifier PXD037921.

Overview and considerations in bottom-up proteomics

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