Protein identification using top-down

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra

In top-down proteomics, the accurate identification and characterization of proteoform through mass spectrometry represents a critical objective. As a result, achieving accuracy in identification results is essential. Multiple primary structure alterations in proteins generate a diverse range of proteoforms, resulting in an exponential increase in potential proteoform. Moreover, the absence of a definitive reference set complicates the standardization of results. Therefore, enhancing the accuracy of proteoform characterization continues to be a significant challenge. We introduced a ResNeXt-based deep learning model, PrSMBooster, for rescoring proteoform spectrum matches (PrSM) during proteoform characterization. As an ensemble method, PrSMBooster integrates four machine learning models, logistic regression, XGBoost, decision tree, and support vector machine, as weak learners to obtain PrSM features. The basic and latent features of PrSM are subsequently input into the ResNeXt model for final rescoring. To verify the effect and accuracy of the PrSMBooster model in rescoring proteoform characterization, it was compared with the characterization algorithm TopPIC across 47 independent mass spectrometry datasets from various species. The experimental results indicate that in most mass spectrometry datasets, the number of PrSMs obtained after rescoring with PrSMBooster increases at a false discovery rate (FDR) of 1%. Further analysis of the experimental results confirmed that PrSMBooster improves the accuracy of PrSM scoring, generates more mass spectrometry characterization results, and demonstrates strong generalization ability.

Deciphering Proteoform Landscape of Mammary Carcinoma by Top-Down Proteomics

Defining the proteoform landscape of breast cancer can provide unique insights into the signaling pathways driving disease progression. While bottom-up proteomics has been utilized to profile breast cancer, it lacks the ability to capture intact proteoforms that may underpin the disease. Top-down proteomics is ideally suited to characterize intact proteoforms; however, most top-down proteomics studies have been limited to low molecular weight (MW) proteins (<50 kDa). Herein, we employed a two-dimensional (2D) liquid chromatography combining size exclusion chromatography (SEC) with reverse phase chromatography (RPC) followed by high-resolution mass spectrometry (MS) to expand the coverage for high MW proteoforms. Using this 2D-SEC-RPC-MS approach, we observed a 5-fold increase in the detection of high MW proteoforms (>50 kDa) compared to the conventional 1D-RPC-MS. SEC separation significantly enhanced the detection of high MW proteoforms (>104 kDa), including intermediate filament proteins, vimentin and keratins. Based on accurate mass measurements and MS/MS data, we identified 775 proteoforms from both TFA and HEPES extracts and detected PTMs, such as acetylation, glutathionylation, and myristoylation. Pathway analysis uncovered many proteoforms involved in processes dysregulated in cancer progression. Overall, our findings illustrate the power of top-down proteomics in defining the proteoform landscape of breast carcinoma.

FAST MS: Software for the Automated Analysis of Top-Down Mass Spectra of Polymeric Molecules Including RNA, DNA, and Proteins

Top-down mass spectrometry (MS) enables comprehensive characterization of modified proteins and nucleic acids and, when native electrospray ionization (ESI) is used, binding site mapping of their complexes with native or therapeutic ligands. However, the high complexity of top-down MS spectra poses a serious challenge to both manual and automated data interpretation, even when the protein, RNA, or DNA sequence and the type of modification or the ligand are known. Here, we introduce FAST MS, a user-friendly software that identifies, assigns and relatively quantifies signals of molecular and fragment ions in MS and MS/MS spectra of biopolymers with known sequence and provides a toolbox for statistical analysis. FAST MS searches mass spectra for ion signals by comparing all signals in the spectrum with isotopic profiles calculated from known sequences, resulting in superior sensitivity and an increased number of assigned fragment ions compared to algorithms that rely on artificial monomer units while maintaining the false positive rate on a moderate level (<5%). FAST MS is an open-source, cross-platform software for the accurate identification, localization and relative quantification of modifications, even in complex mixtures of positional isomers of proteins, oligonucleotides, or any other user-defined linear polymer.

Improving Top-Down Sequence Coverage with Targeted Fragment Matching

Top-down mass spectrometry (TDMS) of intact proteins and antibodies enables direct determination of truncations, sequence variants, post-translational modifications, and disulfides without the need for any proteolytic cleavage. While mass deconvolution of top-down tandem mass spectra is typically used to identify fragment masses for matching to candidate proteoforms, larger molecules such as monoclonal antibodies can produce many fragment ions, making spectral interpretation challenging. Here, we explore an alternative approach for proteoform spectral matching that is better suited for larger protein analysis. This workflow uses direct matching of theoretical proteoform isotopic distributions to TDMS spectra, avoiding drawbacks of mass deconvolution such as poor sensitivity and problems differentiating overlapping distributions. Using a data set that analyzed an intact NIST monoclonal antibody across different fragmentation modes, we show that this isotope fitting strategy increased the sequence coverage of both light and heavy chain sequences >3-fold. We further found that isotope fitting is particularly amenable to identifying large fragments, including those near the hinge region that have been traditionally difficult to analyze by top-down methods. These advances in proteoform spectral matching can greatly increase the power of top-down analyses for intact biotherapeutics and other large molecules.

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.

MSModDetector: a tool for detecting mass shifts and post-translational modifications in individual ion mass spectrometry data

MOTIVATION

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs on the same protein molecule, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even PTMs at distant sites to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.

RESULTS

We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I2MS) data. I2MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I2MS data and experimental I2MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/marjanfaizi/MSModDetector.

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.

Web of venom: exploration of big data resources in animal toxin research

Research on animal venoms and their components spans multiple disciplines, including biology, biochemistry, bioinformatics, pharmacology, medicine, and more. Manipulating and analyzing the diverse array of data required for venom research can be challenging, and relevant tools and resources are often dispersed across different online platforms, making them less accessible to nonexperts. In this article, we address the multifaceted needs of the scientific community involved in venom and toxin-related research by identifying and discussing web resources, databases, and tools commonly used in this field. We have compiled these resources into a comprehensive table available on the VenomZone website (https://venomzone.expasy.org/10897). Furthermore, we highlight the challenges currently faced by researchers in accessing and using these resources and emphasize the importance of community-driven interdisciplinary approaches. We conclude by underscoring the significance of enhancing standards, promoting interoperability, and encouraging data and method sharing within the venom research community.

Recent advances in structural mass spectrometry methods in the context of biosimilarity assessment: from sequence heterogeneities to higher order structures

Biotherapeutics and their biosimilar versions have been flourishing in the biopharmaceutical market for several years. Structural and functional characterization is needed to achieve analytical biosimilarity through the assessment of critical quality attributes as required by regulatory authorities. The role of analytical strategies, particularly mass spectrometry-based methods, is pivotal to gathering valuable information for the in-depth characterization of biotherapeutics and biosimilarity assessment. Structural mass spectrometry methods (native MS, HDX-MS, top-down MS, etc.) provide information ranging from primary sequence assessment to higher order structure evaluation. This review focuses on recent developments and applications in structural mass spectrometry for biotherapeutic and biosimilar characterization.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

Motivation

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.

Results

We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.

Availability

The source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

MASH Native: a unified solution for native top-down proteomics data processing

MOTIVATION

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking.

RESULTS

We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms.

AVAILABILITY AND IMPLEMENTATION

The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

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.

Solid phase extraction as sample pretreatment method for top-down quantitative analysis of low molecular weight proteins from biological samples using liquid chromatography - triple quadrupole mass spectrometry

Targeting and quantifying intact proteins from biological samples is still a very challenging research area. Several crucial steps exist in the analytical workflow, including development of a reliable sample preparation method. Here, we developed and applied for the first time a non-immunoaffinity sample preparation method based on a generally widely available micro-elution solid phase extraction (μSPE) strategy for the extraction of multiple lower molecular weight intact proteins (<30 kDa) from various biological matrices. Omission of a time-consuming drying and reconstitution step after extraction resulted in a more simple and rapid sample preparation procedure. A model set of eleven intact proteins (molecular weights: 5.5-29 kDa; isoelectric points: 4.5-11.3) were analyzed in multiple biological fluids using reversed-phase liquid chromatography with a triple quadrupole mass spectrometer operated in multiple reaction monitoring mode. Various sample pre-treatment reagents, sorbent types, and washing and elution solvents were experimentally tested and optimized to obtain the μSPE clean-up condition for a broad mixture of intact proteins having variable physicochemical properties. 1% trifluoroacetic acid and 0.2% Triton 100-X were selected as suitable sample pre-treatment reagents for releasing protein-protein interactions in human serum/plasma and human urine, respectively. Hydrophilic lipophilic balanced μSPE sorbent was selected as a high performing stationary phase. Addition of 1% trifluoroacetic acid to all washing and elution solutions showed the most beneficial effect for the extraction recovery of the proteins. Under the optimized conditions, reproducible extraction recoveries >65% for all targeted proteins (up to 30 kDa) in human urine and >50% for most of the proteins in serum/plasma were achieved. The selected conditions were applied also for the analysis of clinical serum and urine samples to demonstrate the feasibility of the developed method to target intact proteins directly by more affordable μSPE sample preparation and triple quadrupole mass spectrometry, which could be beneficial in many application fields.

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

mzMD: visualization-oriented MS data storage and retrieval

MOTIVATION

Drawing peaks in a data window of an MS dataset happens at all time in MS data visualization applications. This asks to retrieve from an MS dataset some selected peaks in a data window whose image in a display window reflects the visual feature of all peaks in the data window. If an algorithm for this purpose is asked to output high-quality solutions in real time, then the most fundamental dependence of it is on the storage format of the MS dataset.

RESULTS

We present mzMD, a new storage format of MS datasets and an algorithm to query this format of a storage system for a summary (a set of selected representative peaks) of a given data window. We propose a criterion Q-score to examine the quality of data window summaries. Experimental statistics on real MS datasets verified the high speed of mzMD in retrieving high-quality data window summaries. mzMD reported summaries of data windows whose Q-score outperforms those mzTree reported. The query speed of mzMD is the same as that of mzTree whereas its query speed stability is better than that of mzTree.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at https://github.com/yrm9837/mzMD-java.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteoform identification based on top-down tandem mass spectra with peak error corrections

In this paper, we study the problem for finding complex proteoforms from protein databases based on top-down tandem mass spectrum data. The main difficulty to solve the problem is to handle the combinatorial explosion of various alterations on a protein. To overcome the combinatorial explosion of various alterations on a protein, the problem has been formulated as the alignment problem of a proteoform mass graph (PMG) and a spectrum mass graph (SMG). The other important issue is to handle mass errors of peaks in the input spectrum. In previous methods, an error tolerance value is used to handle the mass differences between the matched consecutive nodes/peaks in PMG and SMG. However, such a way to handle mass error can not guarantee that the mass difference between any pairs of nodes in the alignment is approximately the same for both PMG and SMG. It may lead to large error accumulation if positive (or negative) errors occur consecutively for a large number of consecutive matched node pairs. The problem is severe so that some existing software packages include a step to further refine the alignments. In this paper, we propose a new model to handle the mass errors of peaks based on the formulation of the PMG and SMG. Note that the masses of sub-paths on the PMG are theoretical and suppose to be accurate. Our method allows each peak in the input spectrum to have a predefined error range. In the alignment of PMG and SMG, we need to give a correction of the mass for each matched peak within the predefined error range. After the correction, we impose that the mass between any two (not necessarily consecutive) matched nodes in the PMG is identical to that of the corresponding two matched peaks in the SMG. Intuitively, this kind of alignment is more accurate. We design an algorithm to find a maximum number of matched node and peak pairs in the two (PMG and SMG) mass graphs under the new constraint. The obtained alignment can show matched node and peak pairs as well as the corrected positions of peaks. The algorithm works well for moderate size input instances and takes very long time as well as huge size memory for large input size instances. Therefore, we propose an algorithm to do diagonal alignment. The diagonal alignment algorithm can solve large input size instances in reasonable time. Experiments show that our new algorithms can report alignments with much larger number of matched node pairs. The software package and test data sets are available at https://github.com/Zeirdo/TopMGRefine.

Proteoform Identification and Quantification Using Intact Protein Database Search Engine ProteinGoggle

Proteomics studies the proteome of organisms, especially proteins that are differentially expressed under certain physiological or pathological conditions; qualitative identification of protein sequences and posttranslational modifications (PTMs) and their positions can help us systematically understand the structure and function of proteoforms. With the development and relative popularity of soft ionization technology (such as electrospray ionization technology) and high mass measurement accuracy and high-resolution mass spectrometers (such as orbitrap), the mass spectrometry (MS) characterization of complete proteins (the so-called top-down proteomics) has become possible and has gradually become popular. Corresponding database search engines and protein identification bioinformatics tools have also been greatly developed. This chapter provides a brief overview of intact protein database search algorithm "isotopic mass-to-charge ratio and envelope fingerprinting" and search engine ProteinGoggle.

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.

Photochemical Disulfide-Ene Modification Enhances Protein Sequencing and Disulfide Mapping by Mass Spectrometry

A new photochemical disulfide-ene reaction system capable of alkylating protein disulfide bonds in seconds has been established. The system is simple, containing acetone and isopropanol for disulfide reduction under 254 nm UV irradiation and norbornene as a highly efficient alkylation reagent. Enhanced characterization of disulfide-rich proteins with significantly shortened analysis time is demonstrated by coupling the reaction online with mass spectrometry.

PrSM-Level Side-by-Side Comparison of Online LC-MS Methods with Intact Histone H3 and H4 Proteoforms

The heterogeneity of histone H3 proteoforms makes histone H3 top-down analysis challenging. To enhance the detection coverage of the proteoforms, performing liquid chromatography (LC) front-end to mass spectrometry (MS) detection is recommended. Here, using optimized electron-transfer/high-energy collision dissociation (EThcD) parameters, we have conducted a proteoform-spectrum match (PrSM)-level side-by-side comparison of reversed-phase LC-MS (RPLC-MS), "dual-gradient" weak cation-exchange/hydrophilic interaction LC-MS (dual-gradient WCX/HILIC-MS), and "organic-rich" WCX/HILIC-MS on the top-down analyses of H3.1, H3.2, and H4 proteins extracted from a HeLa cell culture. While both dual-gradient WCX/HILIC and organic-rich WCX/HILIC could resolve intact H3 and H4 proteoforms by the number of acetylations, the organic-rich method could enhance the separations of different trimethyl/acetyl near-isobaric H3 proteoforms. In comparison with RPLC-MS, both of the WCX/HILIC-MS methods enhanced the qualities of the H3 PrSMs and remarkably improved the range, reproducibility, and confidence in the identifications of H3 proteoforms.

PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics

PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron).

Novel Strategies to Address the Challenges in Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics is a powerful technology for comprehensively characterizing proteoforms to decipher post-translational modifications (PTMs) together with genetic variations and alternative splicing isoforms toward a proteome-wide understanding of protein functions. In the past decade, top-down proteomics has experienced rapid growth benefiting from groundbreaking technological advances, which have begun to reveal the potential of top-down proteomics for understanding basic biological functions, unraveling disease mechanisms, and discovering new biomarkers. However, many challenges remain to be comprehensively addressed. In this Account & Perspective, we discuss the major challenges currently facing the top-down proteomics field, particularly in protein solubility, proteome dynamic range, proteome complexity, data analysis, proteoform-function relationship, and analytical throughput for precision medicine. We specifically review the major technology developments addressing these challenges with an emphasis on our research group's efforts, including the development of top-down MS-compatible surfactants for protein solubilization, functionalized nanoparticles for the enrichment of low-abundance proteoforms, strategies for multidimensional chromatography separation of proteins, and a new comprehensive user-friendly software package for top-down proteomics. We have also made efforts to connect proteoforms with biological functions and provide our visions on what the future holds for top-down proteomics.

A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations

Background

Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions.

Results

We observe that number and quality of the peptide-spectrum matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that have previously been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence at the proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins.

Conclusions

The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration of transcriptomics data and other sources of genome-level information.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04159-8.

The Defibrillation Conundrum: New Insights into the Mechanisms of Shock-Related Myocardial Injury Sustained from a Life-Saving Therapy

Implantable cardiac defibrillators (ICDs) are recommended to prevent the risk of sudden cardiac death. However, shocks are associated with an increased mortality with a dose response effect, and a strategy of reducing electrical therapy burden improves the prognosis of implanted patients. We review the mechanisms of defibrillation and its consequences, including cell damage, metabolic remodeling, calcium metabolism anomalies, and inflammatory and pro-fibrotic remodeling. Electrical shocks do save lives, but also promote myocardial stunning, heart failure, and pro-arrhythmic effects as seen in electrical storms. Limiting unnecessary implantations and therapies and proposing new methods of defibrillation in the future are recommended.

ClipsMS: An Algorithm for Analyzing Internal Fragments Resulting from Top-Down Mass Spectrometry

Top-down mass spectrometry (TD-MS) of peptides and proteins results in product ions that can be correlated to polypeptide sequence. Fragments can either be terminal fragments, which contain either the N- or the C-terminus, or internal fragments that contain neither termini. Normally, only terminal fragments are assigned due to the computational difficulties of assigning internal fragments. Here we describe ClipsMS, an algorithm that can assign both terminal and internal fragments generated by top-down MS fragmentation. Further, ClipsMS can be used to locate various modifications on the protein sequence. Using ClipsMS to assign TD-MS generated product ions, we demonstrate that for apo-myoglobin, the inclusion of internal fragments increases the sequence coverage up to 78%. Interestingly, many internal fragments cover complementary regions to the terminal fragments that enhance the information that is extracted from a single top-down mass spectrum. Analysis of oxidized apo-myoglobin using terminal and internal fragment matching by ClipsMS confirmed the locations of oxidation sites on the two methionine residues. Internal fragments can be beneficial for top-down protein fragmentation analysis, and ClipsMS can be a valuable tool for assigning both terminal and internal fragments present in a top-down mass spectrum. Data are available via the MassIVE community resource with the identifiers MSV000086788 and MSV000086789.

Functionally Integrated Top-Down Proteomics for Standardized Assessment of Human Induced Pluripotent Stem Cell-Derived Engineered Cardiac Tissues

Three-dimensional (3D) human induced pluripotent stem cell-derived engineered cardiac tissues (hiPSC-ECTs) have emerged as a promising alternative to two-dimensional hiPSC-cardiomyocyte monolayer systems because hiPSC-ECTs are a closer representation of endogenous cardiac tissues and more faithfully reflect the relevant cardiac pathophysiology. The ability to perform functional and molecular assessments using the same hiPSC-ECT construct would allow for more reliable correlation between observed functional performance and underlying molecular events, and thus is critically needed. Herein, for the first time, we have established an integrated method that permits sequential assessment of functional properties and top-down proteomics from the same single hiPSC-ECT construct. We quantitatively determined the differences in isometric twitch force and the sarcomeric proteoforms between two groups of hiPSC-ECTs that differed in the duration of time of 3D-ECT culture. Importantly, by using this integrated method we discovered a new and strong correlation between the measured contractile parameters and the phosphorylation levels of alpha-tropomyosin between the two groups of hiPSC-ECTs. The integration of functional assessments together with molecular characterization by top-down proteomics in the same hiPSC-ECT construct enables a holistic analysis of hiPSC-ECTs to accelerate their applications in disease modeling, cardiotoxicity, and drug discovery. Data are available via ProteomeXchange with identifier PXD022814.

Proteoform Identification by Combining RNA-Seq and Top-Down Mass Spectrometry

In proteogenomic studies, genomic and transcriptomic variants are incorporated into customized protein databases for the identification of proteoforms, especially proteoforms with sample-specific variants. Most proteogenomic research has been focused on combining genomic or transcriptomic data with bottom-up mass spectrometry data. In the last decade, top-down mass spectrometry has attracted increasing attention because of its capacity to identify various proteoforms with alterations. However, top-down proteogenomics, in which genomic or transcriptomic data are combined with top-down mass spectrometry data, has not been widely adopted, and there is still a lack of software tools for top-down proteogenomic data analysis. In this paper, we introduce TopPG, a proteogenomic tool for generating proteoform sequence databases with genetic alterations and alternative splicing events. Experiments on top-down proteogenomic data of DLD-1 colorectal cancer cells showed that TopPG coupled with database search confidently identified proteoforms with sample-specific alterations.

Mass Spectrometry-Based Structural Analysis of Cysteine-Rich Metal-Binding Sites in Proteins with MetaOdysseus R Software

Identification of metal-binding sites in proteins and understanding metal-coupled protein folding mechanisms are aspects of high importance for the structure-to-function relationship. Mass spectrometry (MS) has brought a powerful adjunct perspective to structural biology, obtaining from metal-to-protein stoichiometry to quaternary structure information. Currently, the different experimental and/or instrumental setups usually require the use of multiple data analysis software, and in some cases, they lack some of the main data analysis steps (MS processing, scoring, identification). Here, we present a comprehensive data analysis pipeline that addresses charge-state deconvolution, statistical scoring, and mass assignment for native MS, bottom-up, and native top-down with emphasis on metal–protein complexes. We have evaluated all of the approaches using assemblies of increasing complexity, including free and chemically labeled proteins, from low- to high-resolution MS. In all cases, the results have been compared with common software and proved how MetaOdysseus outperformed them.

MASH Explorer: A Universal Software Environment for Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.

EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution

Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.

Nickel Nanoparticles Induce the Synthesis of a Tumor-Related Polypeptide in Human Epidermal Keratinocytes

Although nickel allergy and carcinogenicity are well known, their molecular mechanisms are still uncertain, thus demanding studies at the molecular level. The nickel carcinogenicity is known to be dependent on the chemical form of nickel, since only certain nickel compounds can enter the cell. This study investigates, for the first time, the cytotoxicity, cellular uptake, and molecular targets of nickel nanoparticles (NiNPs) in human skin cells in comparison with other chemical forms of nickel. The dose-response curve that was obtained for NiNPs in the cytotoxicity assays showed a linear behavior typical of genotoxic carcinogens. The exposure of keratinocytes to NiNPs leads to the release of Ni²⁺ ions and its accumulation in the cytosol. A 6 kDa nickel-binding molecule was found to be synthesized by cells exposed to NiNPs at a dose corresponding to medium mortality. This molecule was identified to be tumor-related p63-regulated gene 1 protein.

Proteoform characterization based on top-down mass spectrometry

Proteins are dominant executors of living processes. Compared to genetic variations, changes in the molecular structure and state of a protein (i.e. proteoforms) are more directly related to pathological changes in diseases. Characterizing proteoforms involves identifying and locating primary structure alterations (PSAs) in proteoforms, which is of practical importance for the advancement of the medical profession. With the development of mass spectrometry (MS) technology, the characterization of proteoforms based on top-down MS technology has become possible. This type of method is relatively new and faces many challenges. Since the proteoform identification is the most important process in characterizing proteoforms, we comprehensively review the existing proteoform identification methods in this study. Before identifying proteoforms, the spectra need to be preprocessed, and protein sequence databases can be filtered to speed up the identification. Therefore, we also summarize some popular deconvolution algorithms, various filtering algorithms for improving the proteoform identification performance and various scoring methods for localizing proteoforms. Moreover, commonly used methods were evaluated and compared in this review. We believe our review could help researchers better understand the current state of the development in this field and design new efficient algorithms for the proteoform characterization.

Top-down Mass Spectrometry of Sarcomeric Protein Post-translational Modifications from Non-human Primate Skeletal Muscle

Sarcomeric proteins, including myofilament and Z-disk proteins, play critical roles in regulating muscle contractile properties. A variety of isoforms and post-translational modifications (PTMs) of sarcomeric proteins have been shown to be associated with modulation of muscle functions and the occurrence of muscle diseases. Non-human primates (NHPs) are excellent research models for sarcopenia, a disease associated with alterations in sarcomeric proteins, due to their marked similarities to humans. However, the sarcomeric proteins in NHP skeletal muscle have not been well characterized. To gain a deeper understanding of sarcomeric proteins in NHP skeletal muscle, we employed top-down mass spectrometry (MS) to conduct a comprehensive analysis on isoforms and PTMs of sarcomeric proteins in rhesus macaque skeletal muscle. We identified 23 protein isoforms with 46 proteoforms of sarcomeric proteins, including 6 isoforms with 18 proteoforms from fast skeletal troponin T. Particularly, for the first time, a novel PDZ/LIM domain protein isoform, PDLIM7, was characterized with a newly identified protein sequence. Moreover, we also identified multiple PTMs on these proteins, including deamidation, methylation, acetylation, tri-methylation, phosphorylation, and S-glutathionylation. Most PTM sites were localized, including Asn13 deamidation on MLC-2S; His73 methylation on αactin; N-terminal acetylation on most identified proteins; N-terminal tri-methylation on MLC-1S, MLC-1F, MLC-2S, and MLC-2F; Ser14 phosphorylation on MLC-2S; and Ser15 and Ser16 phosphorylation on MLC-2F. In summary, a comprehensive characterization of sarcomeric proteins including multiple isoforms and PTMs in NHP skeletal muscle was achieved by analyzing intact proteins in the top-down MS approach.

VisioProt-MS: interactive 2D maps from intact protein mass spectrometry

SUMMARY

VisioProt-MS is designed to summarize and analyze intact protein and top-down proteomics data. It plots the molecular weights of eluting proteins as a function of their retention time, thereby allowing inspection of runs from liquid chromatography coupled to mass spectrometry (LC-MS). It also overlays MS/MS identification results. VisioProt-MS is compatible with outputs from many different top-down dedicated software. To our knowledge, this is the only open source standalone application that allows the dynamic comparison of several MS files, a prerequisite for comparative analysis of different biological conditions. With its dynamic rendering, this user-friendly web application facilitates inspection, comparison and export of publication quality 2 D maps from deconvoluted LC-MS run(s) and top-down proteomics data.

AVAILABILITY AND IMPLEMENTATION

The Shiny-based web application VisioProt-MS is suitable for non-R users. It can be found at https://masstools.ipbs.fr/mstools/visioprot-ms/ and the corresponding scripts are downloadable at https://github.com/mlocardpaulet/VisioProt-MS. It is governed by the CeCILL license (http://www.cecill.info).

Top-Down Proteomics of Medicinal Cannabis

The revised legislation on medicinal cannabis has triggered a surge of research studies in this space. Yet, cannabis proteomics is lagging. In a previous study, we optimised the protein extraction of mature buds for bottom-up proteomics. In this follow-up study, we developed a top-down mass spectrometry (MS) proteomics strategy to identify intact denatured protein from cannabis apical buds. After testing different source-induced dissociation (SID), collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), and electron transfer dissociation (ETD) parameters on infused known protein standards, we devised three LC-MS/MS methods for top-down sequencing of cannabis proteins. Different MS/MS modes produced distinct spectra, albeit greatly overlapping between SID, CID, and HCD. The number of fragments increased with the energy applied; however, this did not necessarily translate into greater sequence coverage. Some precursors were more amenable to fragmentation than others. Sequence coverage decreased as the mass of the protein increased. Combining all MS/MS data maximised amino acid (AA) sequence coverage, achieving 73% for myoglobin. In this experiment, most cannabis proteins were smaller than 30 kD. A total of 46 cannabis proteins were identified with 136 proteoforms bearing different post-translational modifications (PTMs), including the excision of N-terminal M, the N-terminal acetylation, methylation, and acetylation of K resides, and phosphorylation. Most identified proteins are involved in photosynthesis, translation, and ATP production. Only one protein belongs to the phytocannabinoid biosynthesis, olivetolic acid cyclase.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Proteomic Insights into Phycobilisome Degradation, A Selective and Tightly Controlled Process in The Fast-Growing Cyanobacterium Synechococcus elongatus UTEX 2973

Phycobilisomes (PBSs) are large (3-5 megadalton) pigment-protein complexes in cyanobacteria that associate with thylakoid membranes and harvest light primarily for photosystem II. PBSs consist of highly ordered assemblies of pigmented phycobiliproteins (PBPs) and linker proteins that can account for up to half of the soluble protein in cells. Cyanobacteria adjust to changing environmental conditions by modulating PBS size and number. In response to nutrient depletion such as nitrogen (N) deprivation, PBSs are degraded in an extensive, tightly controlled, and reversible process. In Synechococcus elongatus UTEX 2973, a fast-growing cyanobacterium with a doubling time of two hours, the process of PBS degradation is very rapid, with 80% of PBSs per cell degraded in six hours under optimal light and CO₂ conditions. Proteomic analysis during PBS degradation and re-synthesis revealed multiple proteoforms of PBPs with partially degraded phycocyanobilin (PCB) pigments. NblA, a small proteolysis adaptor essential for PBS degradation, was characterized and validated with targeted mass spectrometry. NblA levels rose from essentially 0 to 25,000 copies per cell within 30 min of N depletion, and correlated with the rate of decrease in phycocyanin (PC). Implications of this correlation on the overall mechanism of PBS degradation during N deprivation are discussed.

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

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.

A perspective view of top-down proteomics in snake venom research

The venom produced by snakes contains complex mixtures of pharmacologically active proteins and peptides which play a crucial role in the pathophysiology of snakebite diseases. The deep understanding of venom proteomes can help to improve the treatment of this "neglected tropical disease" (as expressed by the World Health Organization [WHO]) and to develop new drugs. The most widely used technique for venom analysis is liquid chromatography/tandem mass spectrometry (LC/MS/MS)-based bottom-up (BU) proteomics. Considering the fact that multiple multi-locus gene families encode snake venom proteins, the major challenge for the BU proteomics is the limited sequence coverage and also the "protein inference problem" which result in a loss of information for the identification and characterization of toxin proteoforms (genetic variation, alternative mRNA splicing, single nucleotide polymorphism [SNP] and post-translational modifications [PTMs]). In contrast, intact protein measurements with top-down (TD) MS strategies cover almost complete protein sequences, and prove the ability to identify venom proteoforms and to localize their modifications and sequence variations.

A comprehensive pipeline for translational top-down proteomics from a single blood draw

Top-down proteomics (TDP) by mass spectrometry (MS) is a technique by which intact proteins are analyzed. It has become increasingly popDesalting and concentrating GELFrEEular in translational research because of the value of characterizing distinct proteoforms of intact proteins. Compared to bottom-up proteomics (BUP) strategies, which measure digested peptide mixtures, TDP provides highly specific molecular information that avoids the bioinformatic challenge of protein inference. However, the technique has been difficult to implement widely because of inherent limitations of existing sample preparation methods and instrumentation. Recent improvements in proteoform pre-fractionation and the availability of high-resolution benchtop mass spectrometers have made it possible to use high-throughput TDP for the analysis of complex clinical samples. Here, we provide a comprehensive protocol for analysis of a common sample type in translational research: human peripheral blood mononuclear cells (PBMCs). The pipeline comprises multiple workflows that can be treated as modular by the reader and used for various applications. First, sample collection and cell preservation are described for two clinical biorepository storage schemes. Cell lysis and proteoform pre-fractionation by gel-eluted liquid fractionation entrapment electrophoresis are then described. Importantly, instrument setup and liquid chromatography-tandem MS are described for TDP analyses, which rely on high-resolution Fourier-transform MS. Finally, data processing and analysis are described using two different, application-dependent software tools: ProSight Lite for targeted analyses of one or a few proteoforms and TDPortal for high-throughput TDP in discovery mode. For a single sample, the minimum completion time of the entire experiment is 72 h.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.

Simultaneous Quantification of Protein Expression and Modifications by Top-down Targeted Proteomics: A Case of the Sarcomeric Subproteome

Determining changes in protein expression and post-translational modifications (PTMs) is crucial for elucidating cellular signal transduction and disease mechanisms. Conventional antibody-based approaches have inherent problems such as the limited availability of high-quality antibodies and batch-to-batch variation. Top-down mass spectrometry (MS)-based proteomics has emerged as the most powerful method for characterization and quantification of protein modifications. Nevertheless, robust methods to simultaneously determine changes in protein expression and PTMs remain lacking. Herein, we have developed a straightforward and robust top-down liquid chromatography (LC)/MS-based targeted proteomics platform for simultaneous quantification of protein expression and PTMs with high throughput and high reproducibility. We employed this method to analyze the sarcomeric subproteome from various muscle types of different species, which successfully revealed skeletal muscle heterogeneity and cardiac developmental changes in sarcomeric protein isoform expression and PTMs. As demonstrated, this targeted top-down proteomics platform offers an excellent 'antibody-independent' alternative for the accurate quantification of sarcomeric protein expression and PTMs concurrently in complex mixtures, which is generally applicable to different species and various tissue types.

A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry

Top-down mass spectrometry is capable of identifying whole proteoform sequences with multiple post-translational modifications because it generates tandem mass spectra directly from intact proteoforms. Many software tools, such as ProSightPC, MSPathFinder, and TopMG, have been proposed for identifying proteoforms with modifications. In these tools, various methods are employed to estimate the statistical significance of identifications. However, most existing methods are designed for proteoform identifications without modifications, and the challenge remains for accurately estimating the statistical significance of proteoform identifications with modifications. Here we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry. Coupled with TopMG, TopMCMC identified more spectra than the generating function method from an MCF-7 top-down mass spectrometry data set.

Evaluation of top-down mass spectral identification with homologous protein sequences

BACKGROUND

Top-down mass spectrometry has unique advantages in identifying proteoforms with multiple post-translational modifications and/or unknown alterations. Most software tools in this area search top-down mass spectra against a protein sequence database for proteoform identification. When the species studied in a mass spectrometry experiment lacks its proteome sequence database, a homologous protein sequence database can be used for proteoform identification. The accuracy of homologous protein sequences affects the sensitivity of proteoform identification and the accuracy of mass shift localization.

RESULTS

We tested TopPIC, a commonly used software tool for top-down mass spectral identification, on a top-down mass spectrometry data set of Escherichia coli K12 MG1655, and evaluated its performance using an Escherichia coli K12 MG1655 proteome database and a homologous protein database. The number of identified spectra with the homologous database was about half of that with the Escherichia coli K12 MG1655 database. We also tested TopPIC on a top-down mass spectrometry data set of human MCF-7 cells and obtained similar results.

CONCLUSIONS

Experimental results demonstrated that TopPIC is capable of identifying many proteoform spectrum matches and localizing unknown alterations using homologous protein sequences containing no more than 2 mutations.

A graph-based filtering method for top-down mass spectral identification

BACKGROUND

Database search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.

RESULTS

In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.

CONCLUSIONS

Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.

A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra

Background

Top-down homogeneous multiplexed tandem mass (HomMTM) spectra are generated from modified proteoforms of the same protein with different post-translational modification patterns. They are frequently observed in the analysis of ultramodified proteins, some proteoforms of which have similar molecular weights and cannot be well separated by liquid chromatography in mass spectrometry analysis.

Results

We formulate the top-down HomMTM spectral identification problem as the minimum error k-splittable flow problem on graphs and propose a graph-based algorithm for the identification and quantification of proteoforms using top-down HomMTM spectra.

Conclusions

Experiments on a top-down mass spectrometry data set of the histone H4 protein showed that the proposed method identified many proteoform pairs that better explain the query spectra than single proteoforms.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2273-4) contains supplementary material, which is available to authorized users.

Ambient ionisation mass spectrometry for in situ analysis of intact proteins

Ambient surface mass spectrometry is an emerging field which shows great promise for the analysis of biomolecules directly from their biological substrate. In this article, we describe ambient ionisation mass spectrometry techniques for the in situ analysis of intact proteins. As a broad approach, the analysis of intact proteins offers unique advantages for the determination of primary sequence variations and posttranslational modifications, as well as interrogation of tertiary and quaternary structure and protein-protein/ligand interactions. In situ analysis of intact proteins offers the potential to couple these advantages with information relating to their biological environment, for example, their spatial distributions within healthy and diseased tissues. Here, we describe the techniques most commonly applied to in situ protein analysis (liquid extraction surface analysis, continuous flow liquid microjunction surface sampling, nano desorption electrospray ionisation, and desorption electrospray ionisation), their advantages, and limitations and describe their applications to date. We also discuss the incorporation of ion mobility spectrometry techniques (high field asymmetric waveform ion mobility spectrometry and travelling wave ion mobility spectrometry) into ambient workflows. Finally, future directions for the field are discussed.