Top-down analysis of protein samples by de novo sequencing techniques

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.

Top-Down and Middle-Down Mass Spectrometry of Antibodies

Therapeutic antibodies, primarily immunoglobulin G-based monoclonal antibodies, are developed to treat cancer, autoimmune disorders, and infectious diseases. Their large size, structural complexity, and heterogeneity pose significant analytical challenges, requiring the use of advanced characterization techniques. This review traces the 30-year evolution of top-down (TD) and middle-down (MD) mass spectrometry (MS) for antibody analysis, beginning with their initial applications and highlighting key advances and challenges throughout this period. TD MS allows for the analysis of intact antibodies, and MD MS performs analysis of the antibody subunits, even in complex biological samples. Both approaches preserve critical quality attributes such as sequence integrity, post-translational modifications (PTMs), disulfide bonds, and glycosylation patterns. Key milestones in TD and MD MS of antibodies include the use of structure-specific enzymes for subunit generation, the implementation of high-resolution mass spectrometers, and the adoption of non-ergodic ion activation methods such as electron transfer dissociation (ETD), electron capture dissociation (ECD), ultraviolet photodissociation (UVPD), and matrix-assisted laser desorption/ionization in-source decay (MALDI-ISD). The combination of complementary dissociation methods and the use of consecutive ion activation approaches has further enhanced TD/MD MS performance. The current TD MS record of antibody sequencing with terminal product ions is about 60% sequence coverage obtained using the activated ion-ETD approach on a high-resolution MS platform. Current MD MS analyses with about 95% sequence coverage were achieved using combinations of ion activation and dissociation techniques. The review explores TD and MD MS analysis of novel mAb modalities, including antibody-drug conjugates, bispecific antibodies, and endogenous antibodies from biofluids as well as immunoglobulin A and M-type classes. Content.

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.

Validation of De Novo Peptide Sequences with Bottom-Up Tag Convolution

De novo sequencing is indispensable for the analysis of proteins from organisms with unknown genomes, novel splice variants, and antibodies. However, despite a variety of methods developed to this end, distinguishing between the correct interpretation of a mass spectrum and a number of incorrect alternatives often remains a challenge. Tag convolution is computed for a set of peptide sequence tags of a fixed length k generated from the input tandem mass spectra and can be viewed as a generalization of the well-known spectral convolution. We demonstrate its utility for validating de novo peptide sequences by using a set of those generated by the algorithm PepNovo+ from high-resolution bottom-up data sets for carbonic anhydrase 2 and the Fab region of alemtuzumab and indicate its further potential applications.

Interlaboratory Study for Characterizing Monoclonal Antibodies by Top-Down and Middle-Down Mass Spectrometry

The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the current state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product's primary structure accurately and reproducibly. The major objective of the present study is to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up (BU) approaches with regard to confirming protein integrity, sequencing variable domains, avoiding artifacts, and revealing modifications and their locations. We also aim to gather information on the common TD/MD MS methods and practices in the field. A panel of three mAbs was selected and centrally provided to 20 laboratories worldwide for the analysis: Sigma mAb standard (SiLuLite), NIST mAb standard, and the therapeutic mAb Herceptin (trastuzumab). Various MS instrument platforms and ion dissociation techniques were employed. The present study confirms that TD/MD MS tools are available in laboratories worldwide and provide complementary information to the BU approach that can be crucial for comprehensive mAb characterization. The current limitations, as well as possible solutions to overcome them, are also outlined. A primary limitation revealed by the results of the present study is that the expert knowledge in both experiment and data analysis is indispensable to practice TD/MD MS.

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.

Direct Determination of Antibody Chain Pairing by Top-down and Middle-down Mass Spectrometry Using Electron Capture Dissociation and Ultraviolet Photodissociation

One challenge associated with the discovery and development of monoclonal antibody (mAb) therapeutics is the determination of heavy chain and light chain pairing. Advances in MS instrumentation and MS/MS methods have greatly enhanced capabilities for the analysis of large intact proteins yielding much more detailed and accurate proteoform characterization. Consequently, direct interrogation of intact antibodies or F(ab')2 and Fab fragments has the potential to significantly streamline therapeutic mAb discovery processes. Here, we demonstrate for the first time the ability to efficiently cleave disulfide bonds linking heavy and light chains of mAbs using electron capture dissociation (ECD) and 157 nm ultraviolet photodissociation (UVPD). The combination of intact mAb, Fab, or F(ab')2 mass, intact LC and Fd masses, and CDR3 sequence coverage enabled determination of heavy chain and light chain pairing from a single experiment and experimental condition. These results demonstrate the potential of top-down and middle-down proteomics to significantly streamline therapeutic antibody discovery.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

Chemical-Mediated Digestion: An Alternative Realm for Middle-down Proteomics?

Protein digestion in mass spectrometry (MS)-based bottom-up proteomics targets mainly lysine and arginine residues, yielding primarily 0.6-3 kDa peptides for the proteomes of organisms of all major kingdoms. Recent advances in MS technology enable analysis of complex mixtures of increasingly longer (>3 kDa) peptides in a high-throughput manner supporting the development of a middle-down proteomics (MDP) approach. Generating longer peptides is a paramount step in launching an MDP pipeline, but the quest for the selection of a cleaving agent that would provide the desired 3-15 kDa peptides remains open. Recent bioinformatics studies have shown that cleavage at the rarely occurring amino acid residues such as methionine (Met), tryptophan (Trp), or cysteine (Cys) would be suitable for MDP approach. Interestingly, chemical-mediated proteolytic cleavages uniquely allow targeting these rare amino acids, for which no specific proteolytic enzymes are known. Herein, as potential candidates for MDP-grade proteolysis, we have investigated the performance of chemical agents previously reported to target primarily Met, Trp, and Cys residues: CNBr, BNPS-Skatole (3-bromo-3-methyl-2-(2-nitrophenyl)sulfanylindole), and NTCB (2-nitro-5-thiobenzoic acid), respectively. Figures of merit such as digestion reproducibility, peptide size distribution, and occurrence of side reactions are discussed. The NTCB-based MDP workflow has demonstrated particularly attractive performance, and NTCB is put forward here as a potential cleaving agent for further MDP development.

De Novo Sequencing of Peptides from High-Resolution Bottom-Up Tandem Mass Spectra using Top-Down Intended Methods

Despite high-resolution mass spectrometers are becoming accessible for more and more laboratories, tandem (MS/MS) mass spectra are still often collected at a low resolution. And even if acquired at a high resolution, software tools used for their processing do not tend to benefit from that in full, and an ability to specify a relative mass tolerance in this case often remains the only feature the respective algorithms take advantage of. We argue that a more efficient way to analyze high-resolution MS/MS spectra should be with methods more explicitly accounting for the precision level, and sustain this claim through demonstrating that a de novo sequencing framework originally developed for (high-resolution) top-down MS/MS data is perfectly suitable for processing high-resolution bottom-up datasets, even though a top-down like deconvolution performed as the first step will leave in many spectra at most a few peaks.

De Novo Sequencing of Top-Down Tandem Mass Spectra: A Next Step towards Retrieving a Complete Protein Sequence

De novo sequencing of tandem (MS/MS) mass spectra represents the only way to determine the sequence of proteins from organisms with unknown genomes, or the ones not directly inscribed in a genome-such as antibodies, or novel splice variants. Top-down mass spectrometry provides new opportunities for analyzing such proteins; however, retrieving a complete protein sequence from top-down MS/MS spectra still remains a distant goal. In this paper, we review the state-of-the-art on this subject, and enhance our previously developed Twister algorithm for de novo sequencing of peptides from top-down MS/MS spectra to derive longer sequence fragments of a target protein.

Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry and tandem mass spectrometry for peptide de novo amino acid sequencing for a seven-protein mixture by paired single-residue transposed Lys-N and Lys-C digestion

RATIONALE

Bottom-up tandem mass spectrometry (MS/MS) is regularly used in proteomics to identify proteins from a sequence database. De novo sequencing is also available for sequencing peptides with relatively short sequence lengths. We recently showed that paired Lys-C and Lys-N proteases produce peptides of identical mass and similar retention time, but different tandem mass spectra. Such parallel experiments provide complementary information, and allow for up to 100% MS/MS sequence coverage.

METHODS

Here, we report digestion by paired Lys-C and Lys-N proteases of a seven-protein mixture: human hemoglobin alpha, bovine carbonic anhydrase 2, horse skeletal muscle myoglobin, hen egg white lysozyme, bovine pancreatic ribonuclease, bovine rhodanese, and bovine serum albumin, followed by reversed-phase nanoflow liquid chromatography, collision-induced dissociation, and 14.5 T Fourier transform ion cyclotron resonance mass spectrometry.

RESULTS

Matched pairs of product peptide ions of equal precursor mass and similar retention times from each digestion are compared, leveraging single-residue transposed information with independent interferences to confidently identify fragment ion types, residues, and peptides. Selected pairs of product ion mass spectra for de novo sequenced protein segments from each member of the mixture are presented.

CONCLUSIONS

Pairs of the transposed product ions as well as complementary information from the parallel experiments allow for both high MS/MS coverage for long peptide sequences and high confidence in the amino acid identification. Moreover, the parallel experiments in the de novo sequencing reduce false-positive matches of product ions from the single-residue transposed peptides from the same segment, and thereby further improve the confidence in protein identification. Copyright © 2016 John Wiley & Sons, Ltd.