Protein complexes exhibit great diversity in protein membership, post-translational modifications and noncovalent cofactors, enabling them to function as the actuators of many important biological processes. The exposition of these molecular features with current methods lacks either throughput or molecular specificity, ultimately limiting the use of protein complexes as direct analytical targets in a wide range of applications. Here, we apply native proteomics, enabled by a multistage tandem mass spectrometry approach, to characterize 125 intact endogenous complexes and 217 distinct proteoforms derived from mouse heart and human cancer cell lines in discovery mode. The native conditions preserved soluble protein–protein interactions, high-stoichiometry noncovalent cofactors, covalent modifications to cysteines, and, remarkably, superoxide ligands bound to the metal cofactor of superoxide dismutase 2. The data enable precise compositional analysis of protein complexes as they exist in the cell and demonstrate a new approach that uses mass spectrometry as a bridge to structural biology.

Efforts to map the human protein interactome have resulted in information about hundreds to thousands of multi-protein assemblies housed in public repositories, but the molecular characterization and stoichiometry of their protein subunits remains largely unknown. Here, we combined the CORUM and UniProt databases to create candidates for an error-tolerant search engine designed for hierarchical top-down analyses, identification, and scoring of multi-proteoform complexes by native mass spectrometry.

Human biology is tightly linked to proteins, yet most measurements do not precisely determine alternatively spliced sequences or posttranslational modifications. Here, we present the primary structures of ~30,000 unique proteoforms, nearly 10 times more than in previous studies, expressed from 1690 human genes across 21 cell types and plasma from human blood and bone marrow. The results, compiled in the Blood Proteoform Atlas (BPA), indicate that proteoforms better describe protein-level biology and are more specific indicators of differentiation than their corresponding proteins, which are more broadly expressed across cell types. We demonstrate the potential for clinical application, by interrogating the BPA in the context of liver transplantation and identifying cell and proteoform signatures that distinguish normal graft function from acute rejection and other causes of graft dysfunction.

The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the current state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product's primary structure accurately and reproducibly. The major objective of the present study is to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up (BU) approaches with regard to confirming protein integrity, sequencing variable domains, avoiding artifacts, and revealing modifications and their locations. We also aim to gather information

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photoactivation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here, we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.

Top-down proteomics (TDP) allows precise determination/characterization of the different proteoforms derived from the expression of a single gene. In this study, we targeted apolipoprotein A-I (ApoA-I), a mediator of high-density-lipoprotein cholesterol efflux (HDL-E), which is inversely associated with coronary heart disease risk. Absolute ApoA-I concentration and allelic variation only partially explain inter-individual HDL-E variation. Therefore, we hypothesize that differences in HDL-E are associated with the abundances of different ApoA-I proteoforms. Here, we present a targeted TDP methodology to characterize ApoA-I proteoforms in serum samples and compare their abundances between individuals. We characterized eighteen ApoA-I proteoforms using selected-ion monitoring coupled to electron-transfer dissociation mass spectrometry. We then compared the abundances of these proteoforms between two groups of four participants, representing the individuals with highest and lowest HDL-E values within the Chicago Healthy Aging Study (n=420). Six proteoforms showed significantly (p<0.0005) higher intensity in high HDL-E individuals: canonical ApoA-I [fold difference (fd)=1.17], carboxymethylated ApoA-I (fd=1.24) and, with highest difference, four fatty acylated forms: palmitoylated (fd=2.16), oleoylated (fd=2.08), arachidonoylated (fd=2.31) and one bearing two modifications: palmitoylation and truncation (fd=2.13). These results demonstrate translational potential for targeted TDP in revealing, with high sensitivity, associations between inter-individual proteoform variation and physiological differences underlying disease risk.

Background ApoAI (apolipoproteins AI) and apoAII (apolipoprotein AII) are structural and functional proteins of high‐density lipoproteins (HDL) which undergo post‐translational modifications at specific residues, creating distinct proteoforms. While specific post‐translational modifications have been reported to alter apolipoprotein function, the full spectrum of apoAI and apoAII proteoforms and their associations with cardiometabolic phenotype remains unknown. Herein, we comprehensively characterize apoAI and apoAII proteoforms detectable in serum and their post‐translational modifications and quantify their associations with cardiometabolic health indices. Methods and Results Using top‐down proteomics (mass‐spectrometric analysis of intact proteins), we analyzed paired serum samples from 150 CARDIA (Coronary Artery Risk Development in Young Adults) study participants from year 20 and 25 exams. Measuring 15 apoAI and 9 apoAII proteoforms, 6 of which carried novel post‐translational modifications, we quantified associations between percent proteoform abundance and key cardiometabolic indices. Canonical (unmodified) apoAI had inverse associations with HDL cholesterol and HDL‐cholesterol efflux, and positive associations with obesity indices (body mass index, waist circumference), and triglycerides, whereas glycated apoAI showed positive associations with serum glucose and diabetes mellitus. Fatty‐acid‒modified ApoAI proteoforms had positive associations with HDL cholesterol and efflux, and inverse associations with obesity indices and triglycerides. Truncated and dimerized proteoforms of apoAII were associated with HDL cholesterol (positively) and obesity indices (inversely). Several proteoforms had no significant associations with phenotype. Conclusions Associations between apoAI and AII and cardiometabolic indices are proteoform‐specific. These results provide “proof‐of‐concept” that precise chemical characterization of human apolipoproteins will yield improved insights into the complex pathways through which proteins signify and mediate health and disease.