Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

Top-down mass spectrometry of native proteoforms and their complexes: a community study

The combination of native electrospray ionization with top-down fragmentation in mass spectrometry (MS) allows simultaneous determination of the stoichiometry of noncovalent complexes and identification of their component proteoforms and cofactors. Although this approach is powerful, both native MS and top-down MS are not yet well standardized, and only a limited number of laboratories regularly carry out this type of research. To address this challenge, the Consortium for Top-Down Proteomics initiated a study to develop and test protocols for native MS combined with top-down fragmentation of proteins and protein complexes across 11 instruments in nine laboratories. Here we report the summary of the outcomes to provide robust benchmarks and a valuable entry point for the scientific community.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

Charge site manipulation to enhance top-down fragmentation efficiency

In recent years, top-down mass spectrometry has become a widely used approach to study proteoforms; however, improving sequence coverage remains an important goal. Here, two different proteins, α-synuclein and bovine carbonic anhydrase, were subjected to top-down collision-induced dissociation (CID) after electrospray ionisation. Two high-boiling solvents, DMSO and propylene carbonate, were added to the protein solution in low concentration (2%) and the effects on the top-down fragmentation patterns of the proteins were systematically investigated. Each sample was measured in triplicate, which revealed highly reproducible differences in the top-down CID fragmentation patterns in the presence of a solution additive, even if the same precursor charge state was isolated in the quadrupole of the instrument. Further investigation supports the solution condition-dependent selective formation of different protonation site isomers as the underlying cause of these differences. Higher sequence coverage was often observed in the presence of additives, and the benefits of this approach became even more evident when datasets from different solution conditions were combined, as increases up to 35% in cleavage coverage were obtained. Overall, this approach therefore represents a promising opportunity to increase top-down fragmentation efficiency.

Top-down mass spectrometry of native proteoforms and their complexes: A community study

The combination of native electrospray ionisation with top-down fragmentation in mass spectrometry allows simultaneous determination of the stoichiometry of noncovalent complexes and identification of their component proteoforms and co-factors. While this approach is powerful, both native mass spectrometry and top-down mass spectrometry are not yet well standardised, and only a limited number of laboratories regularly carry out this type of research. To address this challenge, the Consortium for Top-Down Proteomics (CTDP) initiated a study to develop and test protocols for native mass spectrometry combined with top-down fragmentation of proteins and protein complexes across eleven instruments in nine laboratories. The outcomes are summarised in this report to provide robust benchmarks and a valuable entry point for the scientific community.

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.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

Impact of Internal Fragments on Top-Down Analysis of Intact Proteins by 193 nm UVPD

193 nm ultraviolet photodissociation (UVPD) allows high sequence coverage to be obtained for intact proteins using terminal fragments alone. However, internal fragments, those that contain neither N- nor C- terminus, are typically ignored, neglecting their potential to bolster characterization of intact proteins. Here, we explore internal fragments generated by 193 nm UVPD for proteins ranging in size from 17-47 kDa and using the ClipsMS algorithm to facilitate searches for internal fragments. Internal fragments were only retained if identified in multiple replicates in order to reduce spurious assignments and to explore the reproducibility of internal fragments generated by UVPD. Inclusion of internal fragment improved sequence coverage by an average of 18% and 32% for UVPD and HCD, respectively, across all proteins and charge states studied. However, only an average of 18% of UVPD internal fragments were identified in two out of three replicates relative to the average number identified across all replicates for all proteins studied. Conversely, for HCD, an average of 63% of internal fragments were retained across replicates. These trends reflect an increased risk of false-positive identifications and a need for caution when considering internal fragments for UVPD. Additionally, proton-transfer charge reduction (PTCR) reactions were performed following UVPD or HCD to assess the impact on internal fragment identifications, allowing up to 20% more fragment ions to be retained across multiple replicates. At this time, it is difficult to recommend the inclusion of the internal fragment when searching UVPD spectra without further work to develop strategies for reducing the possibilities of false-positive identifications. All mass spectra are available in the public repository jPOST with the accession number JPST001885.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Mass spectrometry and the cellular surfaceome

The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.

Improving the Center Section Sequence Coverage of Large Proteins Using Stepped-Fragment Ion Protection Ultraviolet Photodissociation

Ultraviolet photodissociation (UVPD) mass spectrometry has gained attention in recent years for its ability to provide high sequence coverage of intact proteins. However, secondary dissociation of fragment ions, in which fragment ions subjected to multiple laser pulses decompose into small products, is a common phenomenon during UVPD that contributes to limited coverage in the midsection of protein sequences. To counter secondary dissociation, a method involving the application of notched waveforms to modulate the trajectories of fragment ions away from the laser beam, termed fragment ion protection (FIP), was previously developed to reduce the probability of secondary dissociation. This, in turn, increased the number of identified large fragment ions. In the present study, FIP was applied to UVPD of large proteins ranging in size from 29 to 55 kDa, enhancing the abundances of large fragment ions. A stepped-FIP strategy was implemented in which UVPD mass spectra were collected using multiple different amplitudes of the FIP waveforms and then the results from the mass spectra were combined. By using stepped-FIP, the number of fragment ions in the midsections of the sequences increased for all proteins. For example, whereas no fragment ions were identified in the middle section of the sequence for glutamate dehydrogenase (55 kDa, 55+ charge state), 10 sequence ions were identified by using UVPD-FIP.

Detection of Fusobacterium nucleatum subspecies in the saliva of pre-colorectal cancer patients, using tandem mass spectrometry

OBJECTIVE

Rising evidence links Fusobacterium nucleatum (F. nucleatum) with its four subspecies; nucleatum, polymorphum, animalis, and vincentii, with the development of colorectal cancer (CRC) and its precursor colorectal adenoma (CRA). This study aims to optimize a technique for and explore the capability of matrix-assisted laser-desorption ionization-tandem time-of-flight mass spectrometry (MALDI-TOF/TOF MS) to detect F. nucleatum subspecies directly from the saliva samples of CRA patients and controls without preculturing.

DESIGN

Saliva samples were collected from four CRA patients and eight controls. Proteins were extracted and subjected to solid-phase extraction fractionation, enzymatically digested, and analyzed by MALDI-TOF/TOF MS. F. nucleatum subspecies strains were cultured and used as a positive control.

RESULTS

A proteomics approach was developed to identify F. nucleatum subspecies directly from saliva samples. With this approach, the bacterial culturing step, which could take up to seven days, was bypassed. Overall, 157 F. nucleatum subspecies proteins were detected in the saliva samples. F. nucleatum subsp. nucleatum was absent in the patients while detected in half of the controls.

CONCLUSION

This study presents a novel technique for detecting F. nucleatum subspecies from saliva specimens that could later be employed to better understand a potential role of those subspecies in CRC development.

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Top-Down Mass Spectrometry

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes an extensively glycosylated surface spike (S) protein to mediate host cell entry, and the S protein glycosylation plays key roles in altering the viral binding/function and infectivity. However, the molecular structures and glycan heterogeneity of the new O-glycans found on the S protein regional-binding domain (S-RBD) remain cryptic because of the challenges in intact glycoform analysis by conventional bottom-up glycoproteomic approaches. Here, we report the complete structural elucidation of intact O-glycan proteoforms through a hybrid native and denaturing top-down mass spectrometry (MS) approach employing both trapped ion mobility spectrometry (TIMS) quadrupole time-of-flight and ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR)-MS. Native top-down TIMS-MS/MS separates the protein conformers of the S-RBD to reveal their gas-phase structural heterogeneity, and top-down FTICR-MS/MS provides in-depth glycoform analysis for unambiguous identification of the glycan structures and their glycosites. A total of eight O-glycoforms and their relative molecular abundance are structurally elucidated for the first time. These findings demonstrate that this hybrid top-down MS approach can provide a high-resolution proteoform-resolved mapping of diverse O-glycoforms of the S glycoprotein, which lays a strong molecular foundation to uncover the functional roles of their O-glycans. This proteoform-resolved approach can be applied to reveal the structural O-glycoform heterogeneity of emergent SARS-CoV-2 S-RBD variants as well as other O-glycoproteins in general.

COVID-19 deep classification network based on convolution and deconvolution local enhancement

Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.

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.

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Native Top-Down Mass Spectrometry

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes an extensively glycosylated surface spike (S) protein to mediate host cell entry and the S protein glycosylation is strongly implicated in altering viral binding/function and infectivity. However, the structures and relative abundance of the new O-glycans found on the S protein regional-binding domain (S-RBD) remain cryptic because of the challenges in intact glycoform analysis. Here, we report the complete structural characterization of intact O-glycan proteoforms using native top-down mass spectrometry (MS). By combining trapped ion mobility spectrometry (TIMS), which can separate the protein conformers of S-RBD and analyze their gas phase structural variants, with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS analysis, the O-glycoforms of the S-RBD are comprehensively characterized, so that seven O-glycoforms and their relative molecular abundance are structurally elucidated for the first time. These findings demonstrate that native top-down MS can provide a high-resolution proteoform-resolved mapping of diverse O-glycoforms of the S glycoprotein, which lays a strong molecular foundation to uncover the functional roles of their O-glycans. This proteoform-resolved approach can be applied to reveal the structural O-glycoform heterogeneity of emergent SARS-CoV-2 S-RBD variants, as well as other O-glycoproteins in general.

Top-down proteomics: challenges, innovations, and applications in basic and clinical research

Introduction- A better understanding of the underlying molecular mechanism of diseases is critical for developing more effective diagnostic tools and therapeutics toward precision medicine. However, many challenges remain to unravel the complex nature of diseases. Areas covered- Changes in protein isoform expression and post-translation modifications (PTMs) have gained recognition for their role in underlying disease mechanisms. Top-down mass spectrometry (MS)-based proteomics is increasingly recognized as an important method for the comprehensive characterization of proteoforms that arise from alternative splicing events and/or PTMs for basic and clinical research. Here, we review the challenges, technological innovations, and recent studies that utilize top-down proteomics to elucidate changes in the proteome with an emphasis on its use to study heart diseases. Expert opinion- Proteoform-resolved information can substantially contribute to the understanding of the molecular mechanisms underlying various diseases and for the identification of novel proteoform targets for better therapeutic development . Despite the challenges of sequencing intact proteins, top-down proteomics has enabled a wealth of information regarding protein isoform switching and changes in PTMs. Continuous developments in sample preparation, intact protein separation, and instrumentation for top-down MS have broadened its capabilities to characterize proteoforms from a range of samples on an increasingly global scale.

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.