Expanding Proteoform Identifications in Top-Down Proteomic Analyses by Constructing Proteoform Families

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.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Proteoform Analysis and Construction of Proteoform Families in Proteoform Suite

Proteoform Suite is an interactive software program for the identification and quantification of intact proteoforms from mass spectrometry data. Proteoform Suite identifies proteoforms observed by intact-mass (MS1) analysis. In intact-mass analysis, unfragmented experimental proteoforms are compared to a database of known proteoform sequences and to one another, searching for mass differences corresponding to well-known post-translational modifications or amino acids. Intact-mass analysis enables proteoforms observed in the MS1 data without MS/MS (MS2) fragmentation to be identified. Proteoform Suite further facilitates the construction and visualization of proteoform families, which are the sets of proteoforms derived from individual genes. Bottom-up peptide identifications and top-down (MS2) proteoform identifications can be integrated into the Proteoform Suite analysis to increase the sensitivity and accuracy of the analysis. Proteoform Suite is open source and freely available at https://github.com/smith-chem-wisc/proteoform-suite .

Accurate Proteoform Identification and Quantitation Using pTop 2.0

The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics.

Size Exclusion Chromatography Strategies and MASH Explorer for Large Proteoform Characterization

Top-down mass spectrometry (MS)-based analysis of larger proteoforms (>50 kDa) is typically challenging due to an exponential decay in the signal-to-noise ratio with increasing protein molecular weight (MW) and coelution with low-MW proteoforms. Size exclusion chromatography (SEC) fractionates proteins based on their size, separating larger proteoforms from those of smaller size in the proteome. In this protocol, we initially describe the use of SEC to fractionate high-MW proteoforms from low-MW proteoforms. Subsequently, the SEC fractions containing the proteoforms of interest are subjected to reverse-phase liquid chromatography (RPLC) coupled online with high-resolution MS. Finally, proteoforms are characterized using MASH Explorer, a user-friendly software environment for in-depth proteoform characterization.

MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications

Top-down proteomics analyzes intact proteoforms with all of their post-translational modifications and genetic and RNA splice variants. In addition, modifications introduced either deliberately or inadvertently during sample preparation, that is, via oxidation, alkylation, or labeling reagents, or through the formation of noncovalent adducts (e.g., detergents) further increase the sample complexity. To facilitate the recognition of protein modifications introduced during top-down analysis, we developed MSTopDiff, a software tool with a graphical user interface written in Python, which allows one to detect protein modifications by calculating and visualizing mass differences in top-down data without the prerequisite of a database search. We demonstrate the successful application of MSTopDiff for the detection of artifacts originating from oxidation, formylation, overlabeling during isobaric labeling, and adduct formation with cations or sodium dodecyl sulfate. MSTopDiff offers several modes of data representation using deconvoluted MS1 or MS2 spectra. In addition to artificial modifications, the tool enables the visualization of biological modifications such as phosphorylation and acetylation. MSTopDiff provides an overview of the artificial and biological modifications in top-down proteomics samples, which makes it a valuable tool in quality control of standard workflows and for parameter evaluation during method development.

Automated Assignment of Proteoform Classification Levels

Proteoform identification is required to fully understand the biological diversity present in a sample. However, these identifications are often ambiguous because of the challenges in analyzing full length proteins by mass spectrometry. A five-level proteoform classification system was recently developed to delineate the ambiguity of proteoform identifications and to allow for comparisons across software platforms and acquisition methods. Widespread adoption of this system requires software tools to provide classification of the proteoform identifications. We describe here an implementation of the five-level classification system in the software program MetaMorpheus, which provides both bottom-up and top-down identifications. Additionally, we developed a stand-alone program called ProteoformClassifier that allows users to classify proteoform results from any search program, provided that the program writes output that includes the information necessary to evaluate proteoform ambiguity. This stand-alone program includes a small test file and database to evaluate if a given program provides sufficient information to evaluate ambiguity. If the program does not, then ProteoformClassifier provides meaningful feedback to assist developers with implementing the classification system. We tested currently available top-down software programs and found that none of them (other than MetaMorpheus) provided sufficient information regarding identification ambiguity to permit classification.

Mesh Fragmentation Improves Dissociation Efficiency in Top-down Proteomics

Top-down proteomics is a key mass spectrometry-based technology for comprehensive analysis of proteoforms. Proteoforms exhibit multiple high charge states and isotopic forms in full MS scans. The dissociation behavior of proteoforms in different charge states and subjected to different collision energies is highly variable. The current widely employed data-dependent acquisition (DDA) method selects a narrow m/z range (corresponding to a single proteoform charge state) for dissociation from the most abundant precursors. We describe here Mesh, a novel dissociation strategy, to dissociate multiple charge states of one proteoform with multiple collision energies. We show that the Mesh strategy has the potential to generate fragment ions with improved sequence coverage and improve identification ratios in top-down proteomic analyses of complex samples. The strategy is implemented within an open-source instrument control software program named MetaDrive to perform real time deconvolution and precursor selection.

Combinations of histone post-translational modifications

Histones are essential proteins that package the eukaryotic genome into its physiological state of nucleosomes, chromatin, and chromosomes. Post-translational modifications (PTMs) of histones are crucial to both the dynamic and persistent regulation of the genome. Histone PTMs store and convey complex signals about the state of the genome. This is often achieved by multiple variable PTM sites, occupied or unoccupied, on the same histone molecule or nucleosome functioning in concert. These mechanisms are supported by the structures of ‘readers’ that transduce the signal from the presence or absence of PTMs in specific cellular contexts. We provide background on PTMs and their complexes, review the known combinatorial function of PTMs, and assess the value and limitations of common approaches to measure combinatorial PTMs. This review serves as both a reference and a path forward to investigate combinatorial PTM functions, discover new synergies, and gather additional evidence supporting that combinations of histone PTMs are the central currency of chromatin-mediated regulation of the genome.

Construction of Human Proteoform Families from 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Top-Down Proteomic Data

Identification of proteoforms, the different forms of a protein, is important to understand biological processes. A proteoform family is the set of different proteoforms from the same gene. We previously developed the software program Proteoform Suite, which constructs proteoform families and identifies proteoforms by intact-mass analysis. Here, we have applied this approach to top-down proteomic data acquired at the National High Magnetic Field Laboratory 21 tesla Fourier transform ion cyclotron resonance mass spectrometer (data available on the MassIVE platform with identifier MSV000085978). We explored the ability to construct proteoform families and identify proteoforms from the high mass accuracy data that this instrument provides for a complex cell lysate sample from the MCF-7 human breast cancer cell line. There were 2830 observed experimental proteforms, of which 932 were identified, 44 were ambiguous, and 1854 were unidentified. Of the 932 unique identified proteoforms, 766 were identified by top-down MS2 analysis at 1% false discovery rate (FDR) using TDPortal, and 166 were additional intact-mass identifications (∼4.7% calculated global FDR) made using Proteoform Suite. We recently published a proteoform level schema to represent ambiguity in proteoform identifications. We implemented this proteoform level classification in Proteoform Suite for intact-mass identifications, which enables users to determine the ambiguity levels and sources of ambiguity for each intact-mass proteoform identification.

Relevance and proteomics challenge of functional posttranslational modifications in Kinetoplastid parasites

Protozoan parasitic infections are health, social and economic issues impacting both humans and animals, with significant morbidity and mortality worldwide. Protozoan parasites have complicated life cycles with both intracellular and extracellular forms. As a consequence, protozoan adapt to changing environments in part through a dynamic enzyme-catalyzed process leading to reversible posttranslational modifications (PTMs). The characterization by proteomics approaches reveals the critical role of the PTMs of the proteins involved in host-pathogen interaction. The complexity of PTMs characterization is increased by the high diversity, stoichiometry, dynamic and also co-existence of several PTMs in the same moieties which crosstalk between them. Here, we review how to understand the complexity and the essential role of PTMs crosstalk in order to provide a new hallmark for vaccines developments, immunotherapies and personalized medicine. In addition, the importance of these motifs in the biology and biological cycle of kinetoplastid parasites is highlighted with key examples showing the potential to act as targets against protozoan diseases.

Constructing Human Proteoform Families Using Intact-Mass and Top-Down Proteomics with a Multi-Protease Global Post-Translational Modification Discovery Database

Complex human biomolecular processes are made possible by the diversity of human proteoforms. Constructing proteoform families, groups of proteoforms derived from the same gene, is one way to represent this diversity. Comprehensive, high-confidence identification of human proteoforms remains a central challenge in mass spectrometry-based proteomics. We have previously reported a strategy for proteoform identification using intact-mass measurements, and we have since improved that strategy by mass calibration based on search results, the use of a global post-translational modification discovery database, and the integration of top-down proteomics results with intact-mass analysis. In the present study, we combine these strategies for enhanced proteoform identification in total cell lysate from the Jurkat human T lymphocyte cell line. We collected, processed, and integrated three types of proteomics data (NeuCode-labeled intact-mass, label-free top-down, and multi-protease bottom-up) to maximize the number of confident proteoform identifications. The integrated analysis revealed 5950 unique experimentally observed proteoforms, which were assembled into 848 proteoform families. Twenty percent of the observed proteoforms were confidently identified at a 3.9% false discovery rate, representing 1207 unique proteoforms derived from 484 genes.

Intact-Mass Analysis Facilitating the Identification of Large Human Heart Proteoforms

Proteoforms, the primary effectors of biological processes, are the different forms of proteins that arise from molecular processing events such as alternative splicing and post-translational modifications. Heart diseases exhibit changes in proteoform levels, motivating the development of a deeper understanding of the heart proteoform landscape. Our recently developed two-dimensional top-down proteomics platform coupling serial size exclusion chromatography (sSEC) to reversed-phase chromatography (RPC) expanded coverage of the human heart proteome and allowed observation of high-molecular weight proteoforms. However, most of these observed proteoforms were not identified due to the difficulty in obtaining quality tandem mass spectrometry (MS2) fragmentation data for large proteoforms from complex biological mixtures on a chromatographic time scale. Herein, we sought to identify human heart proteoforms in this data set using an enhanced version of Proteoform Suite, which identifies proteoforms by intact mass alone. Specifically, we added a new feature to Proteoform Suite to determine candidate identifications for isotopically unresolved proteoforms larger than 50 kDa, enabling subsequent MS2 identification of important high-molecular weight human heart proteoforms such as lamin A (72 kDa) and trifunctional enzyme subunit α (79 kDa). With this new workflow for large proteoform identification, endogenous human cardiac myosin binding protein C (140 kDa) was identified for the first time. This study demonstrates the integration of our sSEC-RPC-MS proteomics platform with intact-mass analysis through Proteoform Suite to create a catalog of human heart proteoforms and facilitate the identification of large proteoforms in complex systems.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Identification and Quantification of Proteoforms by Mass Spectrometry

A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, some recent advances are outlined and current challenges and future directions for the field are discussed.

Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics

BACKGROUND

Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data.

RESULTS

Our methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data.

CONCLUSION

Identifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.

Maximizing Selective Cleavages at Aspartic Acid and Proline Residues for the Identification of Intact Proteins

A new approach for the identification of intact proteins has been developed that relies on the generation of relatively few abundant products from specific cleavage sites. This strategy is intended to complement standard approaches that seek to generate many fragments relatively non-selectively. Specifically, this strategy seeks to maximize selective cleavage at aspartic acid and proline residues via collisional activation of precursor ions formed via electrospray ionization (ESI) under denaturing conditions. A statistical analysis of the SWISS-PROT database was used to predict the number of arginine residues for a given intact protein mass and predict a m/z range where the protein carries a similar charge to the number of arginine residues thereby enhancing cleavage at aspartic acid residues by limiting proton mobility. Cleavage at aspartic acid residues is predicted to be most favorable in the m/z range of 1500-2500, a range higher than that normally generated by ESI at low pH. Gas-phase proton transfer ion/ion reactions are therefore used for precursor ion concentration from relatively high charge states followed by ion isolation and subsequent generation of precursor ions within the optimal m/z range via a second proton transfer reaction step. It is shown that the majority of product ion abundance is concentrated into cleavages C-terminal to aspartic acid residues and N-terminal to proline residues for ions generated by this process. Implementation of a scoring system that weights both ion fragment type and ion fragment area demonstrated identification of standard proteins, ranging in mass from 8.5 to 29.0 kDa. Graphical Abstract ᅟ.

Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function

An important goal of the Human Proteome Organization (HUPO) Chromosome-centric Human Proteome Project (C-HPP) is to correctly define the number of canonical proteins encoded by their cognate open reading frames on each chromosome in the human genome. When identified with high confidence of protein evidence (PE), such proteins are termed PE1 proteins in the online database resource, neXtProt. However, proteins that have not been identified unequivocally at the protein level but that have other evidence suggestive of their existence (PE2-4) are termed missing proteins (MPs). The number of MPs has been reduced from 5511 in 2012 to 2186 in 2018 (neXtProt 2018-01-17 release). Although the annotation of the human proteome has made significant progress, the "parts list" alone does not inform function. Indeed, 1937 proteins representing ∼10% of the human proteome have no function either annotated from experimental characterization or predicted by homology to other proteins. Specifically, these 1937 "dark proteins" of the so-called dark proteome are composed of 1260 functionally uncharacterized but identified PE1 proteins, designated as uPE1, plus 677 MPs from categories PE2-PE4, which also have no known or predicted function and are termed uMPs. At the HUPO-2017 Annual Meeting, the C-HPP officially adopted the uPE1 pilot initiative, with 14 participating international teams later committing to demonstrate the feasibility of the functional characterization of large numbers of dark proteins (CP), starting first with 50 uPE1 proteins, in a stepwise chromosome-centric organizational manner. The second aim of the feasibility phase to characterize protein (CP) functions of 50 uPE1 proteins, termed the neXt-CP50 initiative, is to utilize a variety of approaches and workflows according to individual team expertise, interest, and resources so as to enable the C-HPP to recommend experimentally proven workflows to the proteome community within 3 years. The results from this pilot will not only be the cornerstone of a larger characterization initiative but also enhance understanding of the human proteome and integrated cellular networks for the discovery of new mechanisms of pathology, mechanistically informative biomarkers, and rational drug targets.

Identification and Quantification of Murine Mitochondrial Proteoforms Using an Integrated Top-Down and Intact-Mass Strategy

The development of effective strategies for the comprehensive identification and quantification of proteoforms in complex systems is a critical challenge in proteomics. Proteoforms, the specific molecular forms in which proteins are present in biological systems, are the key effectors of biological function. Thus, knowledge of proteoform identities and abundances is essential to unraveling the mechanisms that underlie protein function. We recently reported a strategy that integrates conventional top-down mass spectrometry with intact-mass determinations for enhanced proteoform identifications and the elucidation of proteoform families and applied it to the analysis of yeast cell lysate. In the present work, we extend this strategy to enable quantification of proteoforms, and we examine changes in the abundance of murine mitochondrial proteoforms upon differentiation of mouse myoblasts to myotubes. The integrated top-down and intact-mass strategy provided an increase of ∼37% in the number of identified proteoforms compared to top-down alone, which is in agreement with our previous work in yeast; 1779 unique proteoforms were identified using the integrated strategy compared to 1301 using top-down analysis alone. Quantitative comparison of proteoform differences between the myoblast and myotube cell types showed 129 observed proteoforms exhibiting statistically significant abundance changes (fold change >2 and false discovery rate <5%).

An integrated multipath liquid chromatography-mass spectrometry system for the simultaneous preparation, separation, and detection of proteins and small molecules

A multipath liquid chromatography with mass spectrometry instrument was constructed with the help of restricted access media to online segregate small and large molecules. This liquid chromatography system was custom built with five pumps and three two-position six-port valves to control the flow in a multipath system for the simultaneous analysis of small molecules and proteins. On separate chromatographic channels, small molecules trapped and proteins excluded from the online restricted access media were analyzed downstream using high-efficiency columns and a triple quadrupole mass spectrometer. A model sample, which included five proteins and 22 small molecules with different physicochemical properties, was used to evaluate the system. Following injection, the complete multipath separation and detection was performed in 22 min. Protein exclusion by the restricted access media was not quantitative. Four commercial trap columns were evaluated for their exclusion efficiency toward the proteins. Exclusion efficiency varied from <50% to only a maximum of 75% exclusion across the trap columns tested. An attempt was made to optimize the exclusion efficiency using different flow rates, flow rate gradients, and different additives both in the sample and the mobile phases. Protein exclusion was still erratic and generally nonquantitative.

Proteoform Suite: Software for Constructing, Quantifying, and Visualizing Proteoform Families

We present an open-source, interactive program named Proteoform Suite that uses proteoform mass and intensity measurements from complex biological samples to identify and quantify proteoforms. It constructs families of proteoforms derived from the same gene, assesses proteoform function using gene ontology (GO) analysis, and enables visualization of quantified proteoform families and their changes. It is applied here to reveal systemic proteoform variations in the yeast response to salt stress.