Mass Spectrometry-Based De Novo Sequencing of Monoclonal Antibodies Using Multiple Proteases and a Dual Fragmentation Scheme

Do-It-Yourself De Novo Antibody Sequencing Workflow that Achieves Complete Accuracy of the Variable Regions

Antibodies are widely used as research tools or therapeutic agents. Knowing the sequences of the variable regions of an antibody─both the heavy chain and the light chain─is a prerequisite for the production of recombinant antibodies. Mass spectrometry-based de novo sequencing is a frequently used, and sometimes the only approach to gaining this information. Here, we describe a workflow that enables accurate sequence determination of monoclonal antibodies based on mass spectrometry data and freely available software tools. This workflow, which we developed using a homemade anti-FLAG monoclonal antibody as a reference sample, achieved 100% accuracy of the variable regions with clear distinction between leucine (L) and isoleucine (I). Using this workflow, we successfully decoded a monoclonal anti-HA antibody, for which we had no prior knowledge of its sequence. Based on the de novo sequencing result, we generated a recombinant anti-HA antibody, and demonstrated that it has the same specificity, sensitivity, and affinity as the commercial antibody.

Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry

Antibodies are a major component of adaptive immunity against invading pathogens. Here, we explore possibilities for an analytical approach to characterize the antigen-specific antibody repertoire directly from the secreted proteins in convalescent serum. This approach aims to perform simultaneous antibody sequencing and epitope mapping using a combination of single particle cryo-electron microscopy (cryoEM) and bottom-up proteomics techniques based on mass spectrometry (LC-MS/MS). We evaluate the performance of the deep-learning tool ModelAngelo in determining de novo antibody sequences directly from reconstructed 3D volumes of antibody-antigen complexes. We demonstrate that while map quality is a critical bottleneck, it is possible to sequence antibody variable domains from cryoEM reconstructions with accuracies of up to 80-90%. While the rate of errors exceeds the typical levels of somatic hypermutation, we show that the ModelAngelo-derived sequences can be used to assign the used V-genes. This provides a functional guide to assemble de novo peptides from LC-MS/MS data more accurately and improves the tolerance to a background of polyclonal antibody sequences. Following this proof-of-principle, we discuss the feasibility and future directions of this approach to characterize antigen-specific antibody repertoires.

NME7 maintains primary cilium assembly, ciliary microtubule stability, and Hedgehog signaling

NME7 (nucleoside diphosphate kinase 7), a lesser studied member of the non-metastatic expressed (NME) family, has been reported as a potential subunit of the γ-tubulin ring complex (γTuRC). However, its role in the cilium assembly and function remains unclear. Our research demonstrated that NME7 is located at the centrosome, including at the spindle poles during metaphase and at the basal bodies during cilium assembly. Notably, a small fraction of NME7 localizes within the cilium. Detailed analysis of cilium assembly after NME7 knockdown and knockout revealed that NME7 is required for this process. NME7 knockout cells exhibited sensitivity to nocodazole, indicating its role in ciliary microtubule stability. In addition, NME7 deficiency impacted the Hedgehog signaling pathway, evident from reduced smoothened (Smo) fluorescence within primary cilia. This role of NME7 in Hedgehog signaling may depend on its nucleoside diphosphate kinase activity and γTuRC association. In conclusion, these findings enhance our understanding of the γTuRC roles in primary cilia in mammalian cells, highlighting the importance of NME7 in ciliary functions and signaling pathways.

Epitope and Paratope Mapping of a SUMO-Remnant Antibody Using Cross-Linking Mass Spectrometry and Molecular Docking

The small ubiquitin-like modifier (SUMO) is an important post-translational modification that regulates the function of various proteins essential for DNA damage repair, genome integrity, and cell homeostasis. To identify protein SUMOylation effectively, an enrichment step is necessary, often requiring exogenous gene expression in cells and immunoaffinity purification of SUMO-remnant peptides following tryptic digestion. Previously, an antibody was developed to enrich tryptic peptides containing the remnant NQTGG on the receptor lysine, although the specifics of the structural interaction motif remained unclear. This study integrates de novo sequencing, intact mass spectrometry, cross-linking mass spectrometry, and molecular docking to elucidate the structural interaction motifs of a SUMO-remnant antibody. Additional cross-linking experiments were performed using SUMOylated peptides and high-field asymmetric waveform ion mobility spectrometry (FAIMS) to enhance the sensitivity and confirm interactions at the paratope interface. This study establishes a robust framework for characterizing antibody-antigen interactions, offering valuable insights into the structural basis of SUMO-remnant peptide recognition.

Emerging protein sequencing technologies: proteomics without mass spectrometry?

INTRODUCTION

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry.

AREAS COVERED

We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture.

EXPERT OPINION

Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Decoding Protein Glycosylation by an Integrative Mass Spectrometry-Based De Novo Sequencing Strategy

Glycoproteins, representing more than 50% of human proteins and most biopharmaceuticals, are crucial for regulating various biological processes. The complexity of multiple glycosylation sites often leads to incomplete sequence coverage and ambiguous glycan modification profiles. Here, we developed an integrative mass spectrometry-based approach for decoding unknown glycoproteins, which is featured with the combination of deglycosylation-mediated de novo sequencing with glycosylation site characterization. We utilized the enzymatic deglycosylation of N-/ O-glycans to achieve comprehensive sequence coverage. Additionally, EThcD fragmentation enables the identification of high-quality long peptides, facilitating precise protein assembly. We subsequently applied this method to de novo sequencing of the highly glycosylated therapeutic fusion protein Etanercept (Enbrel). We also sequenced three new tumor necrosis factor receptor:Fc-fusion biologics with largely unknown sequences, unveiling subtle distinctions in the primary sequences. Furthermore, we characterized N- and O-glycosylation modifications of these proteins at subunit, glycopeptide, and glycan levels. This strategy bridges the gap between the de novo sequencing and glycosylation modification, providing comprehensive information on the primary structure and glycosylation modifications for glycoproteins. Notably, our method could be a robust solution for accurate sequencing of the glycoproteins and has practical value not only in basic research but also in the biopharmaceutical industry.

AI-assisted protein design to rapidly convert antibody sequences to intrabodies targeting diverse peptides and histone modifications

Intrabodies are engineered antibodies that function inside living cells, enabling therapeutic, diagnostic, and imaging applications. While powerful, their development has been hindered by challenges associated with their folding, solubility, and stability in the reduced intracellular environment. Here, we present an AI-driven pipeline integrating AlphaFold2, ProteinMPNN, and live-cell screening to optimize antibody framework regions while preserving epitope-binding complementarity-determining regions. Using this approach, we successfully converted 19 out of 26 antibody sequences into functional single-chain variable fragment (scFv) intrabodies, including a panel targeting diverse histone modifications for real-time imaging of chromatin dynamics and gene regulation. Notably, 18 of these 19 sequences had failed to convert using the standard approach, demonstrating the unique effectiveness of our method. As antibody sequence databases expand, our method will accelerate intrabody design, making their development easier, more cost-effective, and broadly accessible for biological research.

Sequencing the monoclonal antibody variable regions using multiple charge integration middle-down strategy and ultraviolet photodissociation

BACKGROUND

Therapeutic monoclonal antibodies (mAbs) have become essential biopharmaceuticals for clinical targeted therapies due to their high specificity, affinity and low side effects. The specificity and affinity of mAb to targeting antigen are mainly dependent on the three complementarity determining regions (CDRs) with high variations in amino acid sequences. Therefore, mAb CDR sequencing is crucial for the characterization of therapeutic mAbs. Here, we developed a 193-nm ultraviolet photodissociation (UVPD) based multiple charge integration middle-down mass spectrometry (MCI-MDMS) strategy for mAb sequencing.

RESULTS

We demonstrate that the UVPD spectra of mAb subunit ions with different charge states exhibit high complementarity, and integration can result in higher sequence coverage compared to single charge states. Finally, over 95 % sequence coverage of two different mAbs has been achieved with full sequence coverage of CDRs, underscoring the great potential of this strategy in accurate sequencing of mAb variable regions. Compared with the conventional higher energy collisional dissociation (HCD) strategy of mAb subunit sequencing, the sequence coverage of CDRs at single UVPD subunit charge state has increased by an average of 30 %. In addition, almost complete sequence coverage of mAb ensures the accurate localization of mAb post-translational modifications (PTMs), including glycosylation of two different sites, C-terminal lysine truncation, and N-terminal cyclization of glutamine.

SIGNIFICANCE

The integration of MCI-MDMS and UVPD realizes high sequence coverage and reliable PTM determination of mAbs. This integrated strategy holds significant promise for accurate analysis of antibody-drug conjugates, polyclonal antibodies and unknown mAbs including sequences and PTMs, and providing a crucial tool for the discovery and development of therapeutic mAbs.

Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

Enhancing De Novo Protein Sequencing through the C-Terminal Labeling Strategy: Resolving Isobaric Ambiguities by Electron-Transfer/Higher Energy Collision Dissociation (EThcD)

De novo protein sequencing via a bottom-up approach requires various proteases to produce overlapping peptides. However, peptides generated by proteases other than trypsin, LysC, and ArgC often yield C-terminal fragments with suboptimal ionization in positive mode mass spectrometry (MS). This study introduces a novel peptide labeling strategy that involves modifying peptides at the C-terminal and at the carboxyl groups of Aspartic and Glutamic acid with arginine methyl ester (R-met) to improve peptide fragmentation and resolve isobaric ambiguities encountered during sequencing. An amidation reaction is used with coupling reagents to conjugate R-met to the peptide's C-terminal end, introducing a functional group that enhances the detectability of C-terminal peptide fragment ions by mass spectrometry. Subsequently, selecting a charge state of +2 or higher can facilitate optimal fragmentation of the derivatized peptides using electron-transfer/higher energy collision dissociation (EThcD), thereby generating essential w-ions to resolve common isobaric ambiguities. Demonstrating this strategy across diverse protein types, including albumin and antibodies and using different proteases for digestion, highlights the unique characteristics of combining the proposed amidation reaction with the specific proteases tested.

De novo protein sequencing of antibodies for identification of neutralizing antibodies in human plasma post SARS-CoV-2 vaccination

The antibody response to vaccination and infection is a key component of the immune response to pathogens. Sequencing of peripheral B cells may not represent the complete B cell receptor repertoire. Here we present a method for sequencing human plasma-derived polyclonal IgG using a combination of mass spectrometry and B-cell sequencing. We investigate the IgG response to the Moderna Spikevax COVID-19 vaccine. From the sequencing data of the natural polyclonal response to vaccination, we generate 12 recombinant antibodies. Six derived recombinant antibodies, including four generated with de novo protein sequencing, exhibit similar or higher binding affinities than the original natural polyclonal antibody. Neutralization tests reveal that the six antibodies possess neutralizing capabilities against the target antigen. This research provides insights into sequencing polyclonal IgG antibodies and the potential of our approach in generating recombinant antibodies with robust binding affinity and neutralization capabilities. Directly examining the circulating IgG pool is crucial due to potential misrepresentations by B-cell analysis alone.

Exploring the versatility of mass spectrometry: Applications across diverse scientific disciplines

Mass spectrometry (MS) has become a pivotal analytical tool across various scientific disciplines due to its ability to provide detailed molecular information with high sensitivity and specificity. MS plays a crucial role in various fields, including drug discovery and development, proteomics, metabolomics, environmental analysis, and clinical diagnostics and Forensic science. In this article we are discussing the application of MS across the diverse scientific disciplines by focusing on some classical examples from each field of application. As the technology continues to evolve, it promises to unlock new possibilities in scientific research and practical applications, cementing its position as an essential tool in modern analytical science.

Characterization of a Novel Monoclonal Antibody with High Affinity and Specificity against Aflatoxins: A Discovery from Rosetta Antibody-Ligand Computational Simulation

Aflatoxin B1 (AFB1) accumulates in crops, where it poses a threat to human health. To detect AFB1, anti-AFB1 monoclonal antibodies have been developed and are widely used. While the sensitivity and specificity of these antibodies have been extensively studied, information regarding the atomic-level docking of AFB1 (and its derivatives) with these antibodies is limited. Such information is crucial for understanding the key interactions that are required for high affinity and specificity in aflatoxin binding. First, a 3D comparative model of anti-AFB1 antibody (Ab-4B5G6) was predicted from the sequence using RosettaAntibody. We then utilized RosettaLigand to dock AFB1 onto ten homology models, producing a total of 10,000 binding modes. Interestingly, the best-scoring mode predicted strong interactions involving four sites within the heavy chain: ALA33, ASN52, HIS95, and TRP99. Importantly, these strong binding interactions exclusively involve the variable domain of the heavy chain. The best-scoring mode with AFB1 was also obtained through AF multimer combined with RosettaLigand, and two interactions at TRP and HIS were consistent with those found by Rosetta antibody-ligand computational simulation. The role of tryptophan in π interactions in antibodies was confirmed through mutation experiments, and the resulting mutant (W99A) exhibited a >1000-fold reduction in binding affinity for AFB1 and analogs, indicating the effect of tryptophan on the stability of CDR-H3 region. Additionally, we evaluated the binding of two glycolic acid-derived molecular derivatives (with impaired hydrogen bonding potential), and these derivatives (AFB2-GA and AFG2-GA) demonstrated a very weak binding affinity for Ab-4B5G6. The heavy chain was successfully isolated, and its sensitivity and specificity were consistent with those of the intact antibody. The homology models of variable heavy (VH) single-domain antibodies were established by RosettaAntibody, and the docking analysis revealed the same residues, including Ala, His, and Trp. Compared to the potential binding mode of fragment variable (FV) region, the results from a model of VH indicated that there are seven models involved in hydrophobic interaction with TYR32, which is usually referred to as polar amino acid and has both hydrophobic and hydrophilic features depending on the circumstances. Our work encompasses the entire process of Rosetta antibody-ligand computational simulation, highlighting the significance of variable heavy domain structural design in enhancing molecular interactions.

Structural Basis for Recognition of the FLAG-tag by Anti-FLAG M2

The FLAG-tag/anti-FLAG system is a widely used biochemical tool for protein detection and purification. Anti-FLAG M2 is the most popular antibody against the FLAG-tag, due to its ease of use, versatility, and availability in pure form or as bead conjugate. M2 binds N-terminal, C-terminal and internal FLAG-tags and binding is calcium-independent, but the molecular basis for the FLAG-tag specificity and recognition remains unresolved. Here we present an atomic resolution (1.17 Å) structure of the FLAG peptide in complex with the Fab of anti-FLAG M2, revealing key binding determinants. Five of the eight FLAG peptide residues form direct interactions with paratope residues. The FLAG peptide adopts a 310 helix conformation in complex with the Fab. These structural insights allowed us to rationally introduce point mutations on both the peptide and antibody side. We tested these by surface plasmon resonance, leading us to propose a shorter yet equally binding version of the FLAG-tag for the M2 antibody.

A Handle on Mass Coincidence Errors in De Novo Sequencing of Antibodies by Bottom-up Proteomics

Antibody sequences can be determined at 99% accuracy directly from the polypeptide product by using bottom-up proteomics techniques. Sequencing accuracy at the peptide level is limited by the isobaric residues leucine and isoleucine, incomplete fragmentation spectra in which the order of two or more residues remains ambiguous due to lacking fragment ions for the intermediate positions, and isobaric combinations of amino acids, of potentially different lengths, for example, GG = N and GA = Q. Here, we present several updates to Stitch (v1.5), which performs template-based assembly of de novo peptides to reconstruct antibody sequences. This version introduces a mass-based alignment algorithm that explicitly accounts for mass coincidence errors. In addition, it incorporates a postprocessing procedure to assign I/L residues based on secondary fragments (satellite ions, i.e., w-ions). Moreover, evidence for sequence assignments can now be directly evaluated with the addition of an integrated spectrum viewer. Lastly, input data from a wider selection of de novo peptide sequencing algorithms are allowed, now including Casanovo, PEAKS, Novor.Cloud, pNovo, and MaxNovo, in addition to flat text and FASTA. Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.

Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel

In temperate and subtropical regions, ancient proteins are reported to survive up to about 2 million years, far beyond the known limits of ancient DNA preservation in the same areas. Accordingly, their amino acid sequences currently represent the only source of genetic information available to pursue phylogenetic inference involving species that went extinct too long ago to be amenable for ancient DNA analysis. Here we present a complete workflow, including sample preparation, mass spectrometric data acquisition and computational analysis, to recover and interpret million-year-old dental enamel protein sequences. During sample preparation, the proteolytic digestion step, usually an integral part of conventional bottom-up proteomics, is omitted to increase the recovery of the randomly degraded peptides spontaneously generated by extensive diagenetic hydrolysis of ancient proteins over geological time. Similarly, we describe other solutions we have adopted to (1) authenticate the endogenous origin of the protein traces we identify, (2) detect and validate amino acid variation in the ancient protein sequences and (3) attempt phylogenetic inference. Sample preparation and data acquisition can be completed in 3-4 working days, while subsequent data analysis usually takes 2-5 days. The workflow described requires basic expertise in ancient biomolecules analysis, mass spectrometry-based proteomics and molecular phylogeny. Finally, we describe the limits of this approach and its potential for the reconstruction of evolutionary relationships in paleontology and paleoanthropology.

Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques

The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual's humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, we incorporated high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, we combined de novo searching and BLAST alignment. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, we determined the uniqueness of tryptic VR peptides and their suitability as antibody surrogate. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.

Reverse-engineering the anti-MUC1 antibody 139H2 by mass spectrometry-based de novo sequencing

Mucin 1 (MUC1) is a transmembrane mucin expressed at the apical surface of epithelial cells at mucosal surfaces. MUC1 has a barrier function against bacterial invasion and is well known for its aberrant expression and glycosylation in adenocarcinomas. The MUC1 extracellular domain contains a variable number of tandem repeats (VNTR) of 20 amino acids, which are heavily O-linked glycosylated. Monoclonal antibodies against the MUC1 VNTR are powerful research tools with applications in the diagnosis and treatment of MUC1-expressing cancers. Here, we report direct mass spectrometry-based sequencing of anti-MUC1 hybridoma-derived 139H2 IgG, enabling reverse-engineering of the functional recombinant monoclonal antibody. The crystal structure of the 139H2 Fab fragment in complex with the MUC1 epitope was solved, revealing the molecular basis of 139H2 binding specificity to MUC1 and its tolerance to O-glycosylation of the VNTR. The available sequence of 139H2 will allow further development of MUC1-related diagnostic, targeting, and treatment strategies.

Immunoglobulin repertoire sequencing and de novo sequencing - Powerful tools for identifying free light chains from patients with light chain cast nephropathy

In patients with light chain cast nephropathy (LCCN), abundantly produced monoclonal immunoglobulin free light chains (FLCs) play a vital role in pathogenesis. Determining the precise sequences of patient-derived FLCs is therefore highly desirable. Although immunoglobulin repertoire sequencing (5' RACE-seq) has been proven to be sensitive enough to provide full-length V(D)J region (variable, diversity and joining genes) of FLCs using bone marrow samples, an invasive and bone marrow independent method is still in demand. Here a de novo sequencing workflow based on the bottom-up proteomics for patient-derived FLCs was established. PEAKS software was used for the de novo sequencing of peptides that were further assembled into full-length FLC sequences. This de novo protein sequencing method can obtain the full-length amino acid sequences of FLCs, and had been shown to be as reliable as 5' RACE-seq. The two LCCN sequences derived from above the two methods were identical, and they possessed more hydrophobic or nonpolar amino acids compared with the corresponding germline, which may be associated with the pathogenesis.

Structural Characterization of a Pathogenic Antibody Underlying Vaccine-Induced Immune Thrombotic Thrombocytopenia (VITT)

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but dangerous side effect of adenoviral-vectored COVID-19 vaccines. VITT had been linked to production of autoantibodies recognizing platelet factor 4 (PF4). Here, we characterize anti-PF4 antibodies obtained from a VITT patient's blood. Intact mass measurements indicate that a significant fraction of these antibodies represent a limited number of clones. MS analysis of large antibody fragments (the light chain and the Fc/2 and Fd fragments of the heavy chain) confirms the monoclonal nature of this component of the anti-PF4 antibodies repertoire and reveals the presence of a mature complex biantennary N-glycan within the Fd segment. Peptide mapping using two complementary proteases and LC-MS/MS was used to determine the amino acid sequence of the entire light chain and over 98% of the heavy chain (excluding a short N-terminal segment). The sequence analysis allows the monoclonal antibody to be assigned to the IgG2 subclass and verifies that the light chain belongs to the λ-type. Incorporation of enzymatic de-N-glycosylation into the peptide mapping routine allows the N-glycan in the Fab region of the antibody to be localized to the framework 3 region of the VH domain. This novel N-glycosylation site is the result of a single mutation within the germline sequence. Peptide mapping also provides information on lower-abundance (polyclonal) components of the anti-PF4 antibody ensemble, revealing the presence of all four subclasses (IgG1-IgG4) and both types of the light chain (λ and κ). This case study demonstrates the power of combining the intact, middle-down, and bottom-up MS approaches for meaningful characterization of ultralow quantities of pathogenic antibodies extracted directly from patients' blood.

Geometric parameters that affect the behavior of logic-gated CAR T cells

Clinical applications of CAR-T cells are limited by the scarcity of tumor-specific targets and are often afflicted with the same on-target/off-tumor toxicities that plague other cancer treatments. A new promising strategy to enforce tumor selectivity is the use of logic-gated, two-receptor systems. One well-described application is termed Tmod™, which originally utilized a blocking inhibitory receptor directed towards HLA-I target antigens to create a protective NOT gate. Here we show that the function of Tmod blockers targeting non-HLA-I antigens is dependent on the height of the blocker antigen and is generally compatible with small, membrane-proximal targets. We compensate for this apparent limitation by incorporating modular hinge units to artificially extend or retract the ligand-binding domains relative to the effector cell surface, thereby modulating Tmod activator and blocker function. By accounting for structural differences between activator and blocker targets, we developed a set of simple geometric parameters for Tmod receptor design that enables targeting of blocker antigens beyond HLA-I, thereby broadening the applications of logic-gated cell therapies.

Species identification of ivory and bone museum objects using minimally invasive proteomics

Ivory is a highly prized material in many cultures since it can be carved into intricate designs and have a highly polished surface. Due to its popularity, the animals from which ivory can be sourced are under threat of extinction. Identification of ivory species is not only important for CITES compliance, it can also provide information about the context in which a work was created. Here, we have developed a minimally invasive workflow to remove minimal amounts of material from precious objects and, using high-resolution mass spectrometry-based proteomics, identified the taxonomy of ivory and bone objects from The Metropolitan Museum of Art collection dating from as early as 4000 B.C. We built a proteomic database of underrepresented species based on exemplars from the American Museum of Natural History, and proposed alternative data analysis workflows for samples containing inconsistently preserved organic material. This application demonstrates extensive ivory species identification using proteomics to unlock sequence uncertainties, e.g., Leu/Ile discrimination.

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

NovoLign: metaproteomics by sequence alignment

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign.

Into the Dark Serum Proteome: Personalized Features of IgG1 and IgA1 Repertoires in Severe COVID-19 Patients

Serum proteomics has matured and is now able to monitor hundreds of proteins quantitatively in large cohorts of patients. However, the fine characteristics of some of the most dominant proteins in serum, the immunoglobulins, are in these studies often ignored, due to their vast, and highly personalized, diversity in sequences. Here, we focus exclusively on these personalized features in the serum proteome and distinctively chose to study individual samples from a low diversity population: elderly donors infected by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). By using mass spectrometry-based methods, immunoglobulin IgG1 and IgA1 clonal repertoires were monitored quantitatively and longitudinally in more than 50 individual serum samples obtained from 17 Corona virus disease 2019 patients admitted to intensive care units. These clonal profiles were used to examine how each patient reacted to a severe SARS-CoV-2 infection. All 17 donors revealed unique polyclonal repertoires and substantial changes over time, with several new clones appearing following the infection, in a few cases leading to a few, very high, abundant clones dominating their repertoire. Several of these clones were de novo sequenced through combinations of top-down, middle-down, and bottom-up proteomics approaches. This revealed sequence features in line with sequences deposited in the SARS-CoV-specific antibody database. In other patients, the serological Ig profiles revealed the treatment with tocilizumab, that subsequently dominated their serological IgG1 repertoire. Tocilizumab clearance could be monitored, and a half-life of approximately 6 days was established. Overall, our longitudinal monitoring of IgG1 and IgA1 repertoires of individual donors reveals that antibody responses are highly personalized traits of each patient, affected by the disease and the chosen clinical treatment. The impact of these observations argues for a more personalized and longitudinal approach in patients' diagnostics, both in serum proteomics as well as in monitoring immune responses.

Mass spectrometry characterization of antibodies at the intact and subunit levels: from targeted to large-scale analysis

Antibodies are one of the most formidable molecular weapons available to our immune system. Their high specificity against a target (antigen) and capability of triggering different immune responses (e.g., complement system activation and antibody-dependent cell-mediated cytotoxicity) make them ideal drugs to fight many different human diseases. Currently, both monoclonal antibodies and more complex molecules based on the antibody scaffold are used as biologics. Naturally, such highly heterogeneous molecules require dedicated analytical methodologies for their accurate characterization. Mass spectrometry (MS) can define the presence and relative abundance of multiple features of antibodies, including critical quality attributes. The combination of small and large variations within a single molecule can only be determined by analyzing intact antibodies or their large (25 to 100 kDa) subunits. Hence, top-down (TD) and middle-down (MD) MS approaches have gained popularity over the last decade. In this Young Scientist Feature we discuss the evolution of TD and MD MS analysis of antibodies, including the new frontiers that go beyond biopharma applications. We will show how this field is now moving from the "quality control" analysis of a known, single antibody to the high-throughput investigation of complex antibody repertoires isolated from clinical samples, where the ultimate goal is represented by the complete gas-phase sequencing of antibody molecules without the need of any a priori knowledge.

Direct Mass Spectrometry-Based Detection and Antibody Sequencing of Monoclonal Gammopathy of Undetermined Significance from Patient Serum: A Case Study

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell disorder characterized by the presence of a predominant monoclonal antibody (i.e., M-protein) in serum, without clinical symptoms. Here we present a case study in which we detect MGUS by liquid-chromatography coupled with mass spectrometry (LC-MS) profiling of IgG1 in human serum. We detected a Fab-glycosylated M-protein and determined the full heavy and light chain sequences by bottom-up proteomics techniques using multiple proteases, further validated by top-down LC-MS. Moreover, the composition and location of the Fab-glycan could be determined in CDR1 of the heavy chain. The outlined approach adds to an expanding mass spectrometry-based toolkit to characterize monoclonal gammopathies such as MGUS and multiple myeloma, with fine molecular detail. The ability to detect monoclonal gammopathies and determine M-protein sequences straight from blood samples by mass spectrometry provides new opportunities to understand the molecular mechanisms of such diseases.

Immunosensor for realtime monitoring of the expression of recombinant proteins during bioprocess

Recombinant protein expression and purification are crucial in modern life sciences research. A fluorescent immunosensor termed Quenchbody (Q-body) was developed for real-time monitoring of FLAG-fused protein expression. Detection results showed that the limit of detection of the 3 × FLAG peptide detected by the TAMRA-labeled anti-FLAG Q-body was as low as 3.1 nM, with a half-maximal effective concentration of 21.4 nM. Furthermore, the anti-FLAG Q-body was used for detecting different proteins fused with a FLAG-tag at the N- or C-terminal. Subsequently, the constructed Q-body was used to monitor the real-time fermentation process of single-strand DNA-binding protein in Escherichia coli. Unlike previously reported Q-bodies that widely used Fab or scFv, the present study used a full-length anti-FLAG IgG for the first time. Owing to its excellent detection speed and sensitivity, the FLAG Q-body immunosensor has the potential to quantify and monitor target recombinant proteins in multiple biological processes in real-time.

[Antibody proteomics]

Antibodies represent an essential component of humoral immunity; therefore their study is important for molecular biology and medicine. The unique property of antibodies to specifically recognize and bind a certain molecular target (an antigen) determines their widespread application in treatment and diagnostics of diseases, as well as in laboratory and biotechnological practices. High specificity and affinity of antibodies is determined by the presence of primary structure variable regions, which are not encoded in the human genome and are unique for each antibody-producing B cell clone. Hence, there is little or no information about amino acid sequences of the variable regions in the databases. This differs identification of antibody primary structure from most of the proteomic studies because it requires either B cell genome sequencing or de novo amino acid sequencing of the antibody. The present review demonstrates some examples of proteomic and proteogenomic approaches and the methodological arsenal that proteomics can offer for studying antibodies, in particular, for identification of primary structure, evaluation of posttranslational modifications and application of bioinformatics tools for their decoding.

Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly

Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.

De novo assisted AFB1-Specific monoclonal antibody sequence assembly and comprehensive molecular characterization

Despite their widely used and access as biological reagents in analytical methods, the detailed structural features for most of the antibodies were rarely known. Here, a new antibody for AFB1 with high specificity in constructing ELISA was studied in detail. The molecular structure and modification were elucidated mainly by nano-electrospray ionization mass spectrometry. The mass experiments, including MALDI-TOF MS, revealed complete and specific fragments, including antibody molecular weight, peptides, glycopeptide, and N-glycoform. By proteolytic treatment of pepsin and trypsin and high-resolution tandem-MS, the primary structure of the newly developed anti-AFB1 antibody was assembled by several rounds of Database search process assisted with the de novo results. The antibody CDR annotation and constraint-based multiple alignment tool were used to differentiate and align the sequences. The method uses only two proteases to generate numerous peptides for de novo sequencing. This artificial assembled AFB1-specific monoclonal antibody sequence was validated by comparison with the sequencing results of the immunoglobulin gene. The results showed that this method achieves full sequence coverage of anti-AFB1 monoclonal antibody, with an accuracy of 100% in the CDR regions of light chain and four amino acid mismatch in heavy chain. This simple and low-cost method was confirmed by treating a public dataset. The secondary structure information of intact antibody was also elucidated from the results of circular dichroism spectrum.

Template-Based Assembly of Proteomic Short Reads For De Novo Antibody Sequencing and Repertoire Profiling

Antibodies can target a vast molecular diversity of antigens. This is achieved by generating a complementary diversity of antibody sequences through somatic recombination and hypermutation. A full understanding of the antibody repertoire in health and disease therefore requires dedicated de novo sequencing methods. Next-generation cDNA sequencing methods have laid the foundation of our current understanding of the antibody repertoire, but these methods share one major limitation in that they target the antibody-producing B-cells, rather than the functional secreted product in bodily fluids. Mass spectrometry-based methods offer an opportunity to bridge this gap between antibody repertoire profiling and bulk serological assays, as they can access antibody sequence information straight from the secreted polypeptide products. In a step to meeting the challenge of mass spectrometry (MS)-based antibody sequencing, we present a fast and simple software tool (Stitch) to map proteomic short reads to user-defined templates with dedicated features for both monoclonal antibody sequencing and profiling of polyclonal antibody repertoires. We demonstrate the use of Stitch by fully reconstructing two monoclonal antibody sequences with >98% accuracy (including I/L assignment); sequencing a Fab from patient serum isolated by reversed-phase liquid chromatography (LC) fractionation against a high background of homologous antibody sequences; sequencing antibody light chains from the urine of multiple-myeloma patients; and profiling the IgG repertoire in sera from patients hospitalized with COVID-19. We demonstrate that Stitch assembles a comprehensive overview of the antibody sequences that are represented in the dataset and provides an important first step toward analyzing polyclonal antibodies and repertoire profiling.

Template-Assisted De Novo Sequencing of SARS-CoV-2 and Influenza Monoclonal Antibodies by Mass Spectrometry

In this study, we used multiple enzyme digestions, coupled with higher-energy collisional dissociation (HCD) and electron-transfer/higher-energy collision dissociation (EThcD) fragmentation to develop a mass-spectrometric (MS) method for determining the complete protein sequence of monoclonal antibodies (mAbs). The method was refined on an mAb of a known sequence, a SARS-CoV-1 antireceptor binding domain (RBD) spike monoclonal antibody. The data were searched using Supernovo to generate a complete template-assisted de novo sequence for this and two SARS-CoV-2 mAbs of known sequences resulting in correct sequences for the variable regions and correct distinction of Ile and Leu residues. We then used the method on a set of 25 antihemagglutinin (HA) influenza antibodies of unknown sequences and determined high confidence sequences for >99% of the complementarity determining regions (CDRs). The heavy-chain and light-chain genes were cloned and transfected into cells for recombinant expression followed by affinity purification. The recombinant mAbs displayed binding curves matching the original mAbs with specificity to the HA influenza antigen. Our findings indicate that this methodology results in almost complete antibody sequence coverage with high confidence results for CDR regions on diverse mAb sequences.

Mass spectrometry-based technologies for probing the 3D world of plant proteins

Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions-which are more difficult to study-dynamically regulate the proteome's 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based "structural" proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.

An Immunoproteomic Survey of the Antibody Landscape: Insights and Opportunities Revealed by Serological Repertoire Profiling

Immunoproteomics has emerged as a versatile tool for analyzing the antibody repertoire in various disease contexts. Until recently, characterization of antibody molecules in biological fluids was limited to bulk serology, which identifies clinically relevant features of polyclonal antibody responses. The past decade, however, has seen the rise of mass-spectrometry-enabled proteomics methods that have allowed profiling of the antibody response at the molecular level, with the disease-specific serological repertoire elucidated in unprecedented detail. In this review, we present an up-to-date survey of insights into the disease-specific immunological repertoire by examining how quantitative proteomics-based approaches have shed light on the humoral immune response to infection and vaccination in pathogenic illnesses, the molecular basis of autoimmune disease, and the tumor-specific repertoire in cancer. We address limitations of this technology with a focus on emerging potential solutions and discuss the promise of high-resolution immunoproteomics in therapeutic discovery and novel vaccine design.

A perspective toward mass spectrometry-based de novo sequencing of endogenous antibodies

A key step in therapeutic and endogenous humoral antibody characterization is identifying the amino acid sequence. So far, this task has been mainly tackled through sequencing of B-cell receptor (BCR) repertoires at the nucleotide level. Mass spectrometry (MS) has emerged as an alternative tool for obtaining sequence information directly at the – most relevant – protein level. Although several MS methods are now well established, analysis of recombinant and endogenous antibodies comes with a specific set of challenges, requiring approaches beyond the conventional proteomics workflows. Here, we review the challenges in MS-based sequencing of both recombinant as well as endogenous humoral antibodies and outline state-of-the-art methods attempting to overcome these obstacles. We highlight recent examples and discuss remaining challenges. We foresee a great future for these approaches making de novo antibody sequencing and discovery by MS-based techniques feasible, even for complex clinical samples from endogenous sources such as serum and other liquid biopsies.

De novo sequencing and native mass spectrometry revealed hetero-association of dirigent protein homologs and potential interacting proteins in Forsythia × intermedia

The discovery of dirigent proteins (DPs) and their functions in plant phenol biochemistry was made over two decades ago with Forsythia × intermedia. Stereo-selective, DP-guided, monolignol-derived radical coupling in vitro was then reported to afford the optically active lignan, (+)-pinoresinol from coniferyl alcohol, provided one-electron oxidase/oxidant capacity was present. It later became evident that DPs have several distinct sub-families, presumably with different functions. Some known DPs require other essential enzymes/proteins (e.g. oxidases) for their functions. However, the lack of a fully sequenced genome for Forsythia × intermedia made it difficult to profile other components co-purified with the (+)-pinoresinol forming DP. Herein, we used an integrated bottom-up, top-down, and native mass spectrometry (MS) approach to de novo sequence the extracted proteins via adaptation of our initial report of DP solubilization and purification. Using publicly available transcriptome and genomic data from closely related species, we identified 14 proteins that were putatively associated with either DP function or the cell wall. Although their co-occurrence after extraction and chromatographic separation is suggestive for potential protein-protein interactions, none were found to form stable protein complexes with DPs in native MS under the specific experimental conditions we have explored. Interestingly, two new DP homologs were found and they formed hetero-trimers. Molecular dynamics simulations suggested that similar hetero-trimers were possible between Arabidopsis DP homologs with comparable sequence similarities. Nevertheless, our integrated mass spectrometry method development helped prepare for future investigations directed to the discovery of novel proteins and protein-protein interactions. These advantages can be highly beneficial for plant and microbial research where fully sequenced genomes may not be readily available.

Human plasma IgG1 repertoires are simple, unique, and dynamic

Although humans can produce billions of IgG1 variants through recombination and hypermutation, the diversity of IgG1 clones circulating in human blood plasma has largely eluded direct characterization. Here, we combined several mass-spectrometry-based approaches to reveal that the circulating IgG1 repertoire in human plasma is dominated by a limited number of clones in healthy donors and septic patients. We observe that each individual donor exhibits a unique serological IgG1 repertoire, which remains stable over time but can adapt rapidly to changes in physiology. We introduce an integrative protein- and peptide-centric approach to obtain and validate a full sequence of an individual plasma IgG1 clone de novo. This IgG1 clone emerged at the onset of a septic episode and exhibited a high mutation rate (13%) compared with the closest matching germline DNA sequence, highlighting the importance of de novo sequencing at the protein level. A record of this paper’s transparent peer review process is included in the supplemental information.

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Novel LC-MS-based methods enable personalized IgG1 profiling in plasma

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Each donor exhibits a simple but unique serological IgG1 repertoire

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This repertoire adapts to changes in physiology, e.g., sepsis

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Individual plasma IgG1 clones can be identified by combining top-down and bottom-up proteomics

De Novo Sequencing of Antibody Light Chain Proteoforms from Patients with Multiple Myeloma

In multiple myeloma diseases, monoclonal immunoglobulin light chains (LCs) are abundantly produced, with, as a consequence in some cases, the formation of deposits affecting various organs, such as the kidney, while in other cases remaining soluble up to concentrations of several g·L^-1 in plasma. The exact factors crucial for the solubility of LCs are poorly understood, but it can be hypothesized that their amino acid sequence plays an important role. Determining the precise sequences of patient-derived LCs is therefore highly desirable. We establish here a novel de novo sequencing workflow for patient-derived LCs, based on the combination of bottom-up and top-down proteomics without database search. PEAKS is used for the de novo sequencing of peptides that are further assembled into full length LC sequences using ALPS. Top-down proteomics provides the molecular masses of proteoforms and allows the exact determination of the amino acid sequence including all posttranslational modifications. This pipeline is then used for the complete de novo sequencing of LCs extracted from the urine of 10 patients with multiple myeloma. We show that for the bottom-up part, digestions with trypsin and Nepenthes digestive fluid are sufficient to produce overlapping peptides able to generate the best sequence candidates. Top-down proteomics is absolutely required to achieve 100% final sequence coverage and characterize clinical samples containing several LCs. Our work highlights an unexpected range of modifications.