Complete De Novo Assembly of Monoclonal Antibody Sequences

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.

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.

Production and characterization of monoclonal antibodies for specific detection of Nosema ceranae and Nosema apis in beehive samples

Two microsporidian species infect honeybees worldwide, Nosema apis and Nosema ceranae. Two different clinical patterns are considered: nosemosis type A (N. apis) and nosemosis type C (N. ceranae). Nosemosis type A is characterized in acute forms and nosemosis type C shows no clear outward clinical signs. The development of a rapid and simple tool for Nosema detection could allow beekeepers or veterinarians to carry out diagnostic tests in situ. Currently, PCR and microscopy are expensive techniques that require qualified staff and may not be available in every laboratory. The present study describes the production and characterization of four monoclonal antibodies (mAbs) against N. ceranae and N. apis, and the development of an IFAT. An IFAT using the mAbs was compared with microscopy and PCR for 180 beehive samples. The diagnostic test revealed similar sensitivity and specificity percentages to IFAT (97.79% and 93.18%, respectively) and microscopy (97.79% and 95.45%), considering 100% for the PCR as the 'gold standard'. A mAb (7D2) was patented for its high specificity for N. ceranae. The IFAT using the mAbs is a good alternative to microscopy and PCR in laboratories where PCR is not available for the detection and identification of both Nosema spp.

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.

Generative AI Models for the Protein Scaffold Filling Problem

De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches used in the field of mass spectrometry to analyze and sequence proteins. However, these approaches often produce incomplete protein sequences with gaps, namely scaffolds. The protein scaffold filling problem refers to filling the missing amino acids in the gaps of a scaffold to infer the complete protein sequence. In this article, we tackle the protein scaffold filling problem based on generative AI techniques, such as convolutional denoising autoencoder, transformer, and generative pretrained transformer (GPT) models, to complete the protein sequences and compare our results with recently developed convolutional long short-term memory-based sequence model. We evaluate the model performance both on a real dataset and generated datasets. All proposed models show outstanding prediction accuracy. Notably, the GPT-2 model achieves 100% gap-filling accuracy and 100% full sequence accuracy on the MabCampth protein scaffold, which outperforms the other models.

Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach

The impact of the world's growing population on food systems and the role of dietary patterns in the management of non-communicable diseases underscore the need to explore sustainable and dietary protein sources. Although microalgae have stood out as alternative sources of proteins and bioactive peptides, some species such as Nannochloropsis gaditana remain unexplored. This study aimed to characterize N. gaditana's proteome and evaluate its potential as a source of bioactive peptides by using an in silico approach. A total of 1955 proteins were identified and classified into functional groups of cellular components, molecular functions, and biological processes. In silico gastrointestinal digestion of identified proteins demonstrated that 202 hydrophobic and low-molecular-size peptides with potential bioactivity were released. Among them, 27 exhibited theorical antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and/or antimicrobial activities. Seven of twenty-seven peptides showed ≥20% intestinal absorption, suggesting potential systemic effects, while the rest could act at local level. Molecular docking demonstrated strong affinities with key enzymes such as MPO, ACE, and DPPIV. Resistance to the digestion, capacity to be absorbed, and multifunctionality were demonstrated for peptide FIPGL. This study highlights N. gaditana's potential as a sustainable source of novel potential bioactive peptides with promising local and systemic biological effects.

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning models often rely on autoregressive generation, which suffers from error accumulation and slow inference speeds. In this work, we introduce π-PrimeNovo, a non-autoregressive Transformer-based model for peptide sequencing. With our architecture design and a CUDA-enhanced decoding module for precise mass control, π-PrimeNovo achieves significantly higher accuracy and up to 89x faster inference than state-of-the-art methods, making it ideal for large-scale applications like metaproteomics. Additionally, it excels in phosphopeptide mining and detecting low-abundance post-translational modifications (PTMs), marking a substantial advance in peptide sequencing with broad potential in biological research.

De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics

The changes in protein expression are hallmarks of development and disease. Protein expression can be established qualitatively and quantitatively using mass spectrometry (MS). Samples are prepared, proteins extracted and then analyzed using MS and MS/MS. The resulting spectra need to be processed computationally to assign peptide spectrum match. Database searches employ sequence databases or spectral libraries for matching possible peptides with the measured spectra. This route is well established but fails when peptides are not found in sequence repositories. In this case, de novo sequencing of MS/MS spectra can be employed. Many computational algorithms that establish the peptide sequence from MS/MS spectrum alone are available. While de novo sequencing assigns a sequence to an MS/MS spectrum, this assignment can be used in further processes for genome annotation. For example, novel exons can be assigned, known exons can be extended, and splice sites can be validated at the protein level. We compiled an extensive list of such algorithms, grouped them, and discussed the selected approaches. We also provide a roadmap of how de novo sequencing can enter mainstream proteogenomic analysis. In the future, de novo predictions can be added to sample-specific protein databases, including RNA-seq translations. These enriched databases can then be used for proteogenomics studies with existing pipelines.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

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.

In silico analysis of the interaction of de novo peptides derived from Salvia hispanica with anticancer targetsEvaluation of the anticancer potential of de novo peptides derived from Salvia hispanica through molecular docking

Cancer is one of the leading causes of death worldwide. Conventional cancer therapies are not selective to cancer cells resulting in serious side effects on patients. Thus, the need for complementary treatments that improve the patient's response to cancer therapy is highly important. To predict and evaluate the physicochemical characteristics and potential anticancer activity of the peptides identified from S. hispanica protein fraction <1 kDa through the use of in silico tools. Peptides derived from Salvia hispanica's protein fraction <1 kDa were identified and analyzed for the prediction of their physicochemical properties. The characterized peptide sequences were then submitted to a multi-criteria decision analysis to identify the peptides that possess the characteristics to potentially exert anticancer activity. Through molecular docking analysis, the potential anticancer activity of the Potentially Anticancer Peptide (PAP)-1, PAP-2, PAP-3, PAP-4, and PAP-5 was estimated by their binding interactions with cancer and apoptosis-related molecules. All five evaluated PAPs exhibited strong binding interactions (< -100 kcal/mol). However, PAP-3 showed the lowest binding free energies with several of the targets. Thus, PAP-3 shows potential to be used as a nutraceutical or ingredient for functional foods that adjuvate in cancer treatment. Conclusions: Through the molecular docking studies, the binding of the PAPs to target molecules of interest for cancer treatment was successfully simulated, from which PAP-3 exhibited the lowest binding free energies. Further in vitro and in vivo studies are required to validate the predictions obtained by the in silico analysis.Communicated by Ramaswamy H. Sarma.

A Hydroxylamine-Mediated Amidination of Lysine Residues That Retains the Protein's Positive Charge

Lysine-specific peptide and protein modification strategies are widely used to study charge-related functions and applications. However, these strategies often result in the loss of the positive charge on lysine, significantly impacting the charge-related properties of proteins. Herein, we report a strategy to preserve the positive charge and selectively convert amines in lysine side chains to amidines using nitriles and hydroxylamine under aqueous conditions. Various unprotected peptides and proteins were successfully modified with a high conversion rate. Moreover, the reactive amidine moiety and derived modification site enable subsequent secondary modifications. Notably, positive charges were retained during the modification. Therefore, positive charge-related protein properties, such as liquid-liquid phase separation behaviour of α-synuclein, were not affected. This strategy was subsequently applied to a lysine rich protein to develop an amidine-containing coacervate DNA complex with outstanding mechanical properties. Overall, our innovative strategy provides a new avenue to explore the characteristics of positively charged proteins.

TRP14 is the rate-limiting enzyme for intracellular cystine reduction and regulates proteome cysteinylation

It has remained unknown how cells reduce cystine taken up from the extracellular space, which is a required step for further utilization of cysteine in key processes such as protein or glutathione synthesis. Here, we show that the thioredoxin-related protein of 14 kDa (TRP14, encoded by TXNDC17) is the rate-limiting enzyme for intracellular cystine reduction. When TRP14 is genetically knocked out, cysteine synthesis through the transsulfuration pathway becomes the major source of cysteine in human cells, and knockout of both pathways becomes lethal in C. elegans subjected to proteotoxic stress. TRP14 can also reduce cysteinyl moieties on proteins, rescuing their activities as here shown with cysteinylated peroxiredoxin 2. Txndc17 knockout mice were, surprisingly, protected in an acute pancreatitis model, concomitant with activation of Nrf2-driven antioxidant pathways and upregulation of transsulfuration. We conclude that TRP14 is the evolutionarily conserved enzyme principally responsible for intracellular cystine reduction in C. elegans, mice, and humans.

PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models

The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.

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.

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.

Evaluation of the Inhibitory Potential of Synthetic Peptides Homologous to CDR3 Regions of a Monoclonal Antibody against Bothropic Venom Serine Proteases

Snakebite accidents, neglected tropical diseases per the WHO, pose a significant public health threat due to their severity and frequency. Envenomation by Bothrops genus snakes leads to severe manifestations due to proteolytic enzymes. While the antibothropic serum produced by the Butantan Institute saves lives, its efficacy is limited as it fails to neutralize certain serine proteases. Hence, developing new-generation antivenoms, like monoclonal antibodies, is crucial. This study aimed to explore the inhibitory potential of synthetic peptides homologous to the CDR3 regions of a monoclonal antibody targeting a snake venom thrombin-like enzyme (SVTLE) from B. atrox venom. Five synthetic peptides were studied, all stable against hydrolysis by venoms and serine proteases. Impressively, four peptides demonstrated uncompetitive SVTLE inhibition, with Ki values ranging from 10-6 to 10-7 M. These findings underscore the potential of short peptides homologous to CDR3 regions in blocking snake venom toxins, suggesting their promise as the basis for new-generation antivenoms. Thus, this study offers potential advancements in combatting snakebites, addressing a critical public health challenge in tropical and subtropical regions.

What is the role of microbial biotechnology and genetic engineering in medicine?

Microbial products are essential for developing various therapeutic agents, including antibiotics, anticancer drugs, vaccines, and therapeutic enzymes. Genetic engineering techniques, functional genomics, and synthetic biology unlock previously uncharacterized natural products. This review highlights major advances in microbial biotechnology, focusing on gene-based technologies for medical applications.

Substrate profiling of the metalloproteinase ovastacin uncovers specific enzyme-substrate interactions and discloses fertilization-relevant substrates

The metalloproteinase ovastacin is released by the mammalian egg upon fertilization and cleaves a distinct peptide bond in zona pellucida protein 2 (ZP2), a component of the enveloping extracellular matrix. This limited proteolysis causes zona pellucida hardening, abolishes sperm binding, and thereby regulates fertility. Accordingly, this process is tightly controlled by the plasma protein fetuin-B, an endogenous competitive inhibitor. At present, little is known about how the cleavage characteristics of ovastacin differ from closely related proteases. Physiological implications of ovastacin beyond ZP2 cleavage are still obscure. In this study, we employed N-terminal amine isotopic labeling of substrates (N-TAILS) contained in the secretome of mouse embryonic fibroblasts to elucidate the substrate specificity and the precise cleavage site specificity. Furthermore, we were able to unravel the physicochemical properties governing ovastacin-substrate interactions as well as the individual characteristics that distinguish ovastacin from similar proteases, such as meprins and tolloid. Eventually, we identified several substrates whose cleavage could affect mammalian fertilization. Consequently, these substrates indicate newly identified functions of ovastacin in mammalian fertilization beyond zona pellucida hardening.

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.

The Time-Resolved Salt Stress Response of Dunaliella tertiolecta-A Comprehensive System Biology Perspective

Algae-driven processes, such as direct CO2 fixation into glycerol, provide new routes for sustainable chemical production in synergy with greenhouse gas mitigation. The marine microalgae Dunaliella tertiolecta is reported to accumulate high amounts of intracellular glycerol upon exposure to high salt concentrations. We have conducted a comprehensive, time-resolved systems biology study to decipher the metabolic response of D. tertiolecta up to 24 h under continuous light conditions. Initially, due to a lack of reference sequences required for MS/MS-based protein identification, a high-quality draft genome of D. tertiolecta was generated. Subsequently, a database was designed by combining the genome with transcriptome data obtained before and after salt stress. This database allowed for detection of differentially expressed proteins and identification of phosphorylated proteins, which are involved in the short- and long-term adaptation to salt stress, respectively. Specifically, in the rapid salt adaptation response, proteins linked to the Ca2+ signaling pathway and ion channel proteins were significantly increased. While phosphorylation is key in maintaining ion homeostasis during the rapid adaptation to salt stress, phosphofructokinase is required for long-term adaption. Lacking β-carotene, synthesis under salt stress conditions might be substituted by the redox-sensitive protein CP12. Furthermore, salt stress induces upregulation of Calvin-Benson cycle-related proteins.

Complete Genome Sequencing and Bacteriocin Functional Characterization of Pediococcus ethanolidurans CP201 from Daqu

This study aims to sequence the whole genome of Pediococcus ethanolidurans CP201 isolated from Daqu and determine the anti-corrosion ability of bacteriocins on chicken breast. The whole genome sequence information of P. ethanolidurans CP201 was analyzed, and its gene structure and function were explored. It was found that gene1164 had annotations in the NR, Pfam, and Swiss-Prot databases, and was related to bacteriocins. The exogenous expression of the bacteriocin gene Pediocin PE-201 was analyzed based on the pET-21b vector and the host BL21, and the corresponding bacteriocin was successfully expressed under the induction of IPTG. After purification by NI-NTA column, enterokinase treatment, membrane dialysis concentration treatment, and SDS-PAGE electrophoresis, the molecular weight was about 6.5 kDa and the purity was above 90%. By applying different concentrations of bacteriocin to chicken breast with different levels of contamination, the control of pathogenic bacteria, the ordinary contamination level (OC) group, and the high contamination level (MC) group could be completely achieved with 25 mg/L bacteriocin. In conclusion, the bacteriocin produced by the newly isolated CP201 can be applied to the preservation of meat products to prevent the risk of food-borne diseases.

Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Adaptation of Proteome and Metabolism in Different Haplotypes of Rhodosporidium toruloides during Cu(I) and Cu(II) Stress

Rhodosporidium toruloides is a carotenogenic, oleogenic yeast that is able to grow in diverse environments. In this study, the proteomic and metabolic responses to copper stress in the two haplotypes IFO0559 and IFO0880 were assessed. 0.5 mM Cu(I) extended the lag phase of both strains significantly, while only a small effect was observed for Cu(II) treatment. Other carotenogenic yeasts such as Rhodotorula mucilaginosa are known to accumulate high amounts of carotenoids as a response to oxidative stress, posed by excess copper ion activity. However, no significant increase in carotenoid accumulation for both haplotypes of R. toruloides after 144 h of 0.5 mM Cu(I) or Cu(II) stress was observed. Yet, an increase in lipid production was detected, when exposed to Cu(II), additionally, proteins related to fatty acid biosynthesis were detected in increased amounts under stress conditions. Proteomic analysis revealed that besides the activation of the enzymatic oxidative stress response, excess copper affected iron–sulfur and zinc-containing proteins and caused proteomic adaptation indicative of copper ion accumulation in the vacuole, mitochondria, and Golgi apparatus.

Antibody sequences assembly method based on weighted de Bruijn graph

With the development of next-generation protein sequencing technologies, sequence assembly algorithm has become a key technology for de novo sequencing process. At present, the existing methods can address the assembly of an unknown single protein chain. However, for monoclonal antibodies with light and heavy chains, the assembly is still an unsolved question. To address this problem, we propose a new assembly method, DBAS, which integrates the quality scores and sequence alignment scores from de novo sequencing peptides into a weighted de Bruijn graph to assemble the final protein sequences. The established method is used to assembling sequences from two datasets with mixed light and heavy chains from antibodies. The results show that the DBAS can assemble long antibody sequences for both mixed light and heavy chains and single chains. In addition, DBAS is able to distinguish the light and heavy chains by using BLAST sequence alignment. The results show that the algorithm has good performance for both target sequence coverage and contig assembly accuracy.

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.

Cross-linking reactions in food proteins and proteomic approaches for their detection

Various protein cross-linking reactions leading to molecular polymerization and covalent aggregates have been described in processed foods. They are an undesired side effect of processes designed to reduce bacterial load, extend shelf life, and modify technological properties, as well as being an expected result of treatments designed to modify raw material texture and function. Although the formation of these products is known to affect the sensory and technological properties of foods, the corresponding cross-linking reactions and resulting protein polymers have not yet undergone detailed molecular characterization. This is essential for describing how their generation can be related to food processing conditions and quality parameters. Due to the complex structure of cross-linked species, bottom-up proteomic procedures developed to characterize various amino acid modifications associated with food processing conditions currently offer a limited molecular description of bridged peptide structures. Recent progress in cross-linking mass spectrometry for the topological characterization of protein complexes has facilitated the development of various proteomic methods and bioinformatic tools for unveiling bridged species, which can now also be used for the detailed molecular characterization of polymeric cross-linked products in processed foods. We here examine their benefits and limitations in terms of evaluating cross-linked food proteins and propose future scenarios for application in foodomics. They offer potential for understanding the protein cross-linking formation mechanisms in processed foods, and how the inherent beneficial properties of treated foodstuffs can be preserved or enhanced.

Vascular Proteome Responses Precede Organ Dysfunction in a Murine Model of Staphylococcus aureus Bacteremia

Vascular dysfunction and organ failure are two distinct, albeit highly interconnected, clinical outcomes linked to morbidity and mortality in human sepsis. The mechanisms driving vascular and parenchymal damage are dynamic and display significant molecular cross talk between organs and tissues. Therefore, assessing their individual contribution to disease progression is technically challenging. Here, we hypothesize that dysregulated vascular responses predispose the organism to organ failure. To address this hypothesis, we have evaluated four major organs in a murine model of Staphylococcus aureus sepsis by combining in vivo labeling of the endothelial cell surface proteome, data-independent acquisition (DIA) mass spectrometry, and an integrative computational pipeline. The data reveal, with unprecedented depth and throughput, that a septic insult evokes organ-specific proteome responses that are highly compartmentalized, synchronously coordinated, and significantly correlated with the progression of the disease. These responses include abundant vascular shedding, dysregulation of the intrinsic pathway of coagulation, compartmentalization of the acute phase response, and abundant upregulation of glycocalyx components. Vascular cell surface proteome changes were also found to precede bacterial invasion and leukocyte infiltration into the organs, as well as to precede changes in various well-established cellular and biochemical correlates of systemic coagulopathy and tissue dysfunction. Importantly, our data suggest a potential role for the vascular proteome as a determinant of the susceptibility of the organs to undergo failure during sepsis. IMPORTANCE Sepsis is a life-threatening response to infection that results in immune dysregulation, vascular dysfunction, and organ failure. New methods are needed for the identification of diagnostic and therapeutic targets. Here, we took a systems-wide approach using data-independent acquisition (DIA) mass spectrometry to track the progression of bacterial sepsis in the vasculature leading to organ failure. Using a murine model of S. aureus sepsis, we were able to quantify thousands of proteins across the plasma and parenchymal and vascular compartments of multiple organs in a time-resolved fashion. We showcase the profound proteome remodeling triggered by sepsis over time and across these compartments. Importantly, many vascular proteome alterations precede changes in traditional correlates of organ dysfunction, opening a molecular window for the discovery of early markers of sepsis progression.

Effects of Light on Growth and Metabolism of Rhodococcus erythropolis

Rhodococcus erythropolis is resilient to various stressors. However, the response of R. erythropolis towards light has not been evaluated. In this study, R. erythropolis was exposed to different wavelengths of light. Compared to non-illuminated controls, carotenoid levels were significantly increased in white (standard warm white), green (510 nm) and blue light (470 nm) illuminated cultures. Notably, blue light (455, 425 nm) exhibited anti-microbial effects. Interestingly, cellular lipid composition shifted under light stress, increasing odd chain fatty acids (C15:0, C17:1) cultured under white (standard warm white) and green (510 nm) light. When exposed to blue light (470, 455, 425 nm), fatty acid profiles shifted to more saturated fatty acids (C16:1 to C16:0). Time-resolved proteomics analysis revealed several oxidative stress-related proteins to be upregulated under light illumination.

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.

Sensoproteomic Discovery of Taste-Modulating Peptides and Taste Re-engineering of Soy Sauce

Soy sauce, one of the most common Asian fermented foods, exhibits a distinctive savory taste profile. In the present study, targeted quantitation of literature-known taste compounds, calculation of dose-over-threshold factors, and taste re-engineering experiments enabled the identification of 34 key tastants. Following the sensoproteomics approach, 14 umami-, kokumi-, and salt-enhancing peptides were identified for the first time, with intrinsic taste threshold concentrations in the range of 166-939 μmol/L and taste-modulating threshold concentrations ranging from 42 to 420 μmol/L. The lowest taste-modulating threshold concentrations were found for the leucyl peptide LDYY with an umami- and salt-enhancing threshold of 42 μmol/L. Addition of the 14 newly identified peptides to the taste recombinate (aRec_Dipeptides) increased the overall taste intensity and mouthfulness of the recombinate, and comparison with the authentic soy sauce confirmed the identification of all key tastants. Finally, these data as well as the quantitative profiling of several (non)-fermented foods highlight the importance of fermentation with respect to taste formation. On the basis of this knowledge, microorganisms with specific digestion patterns may be used to tailor the taste profile and especially the salt taste sensation of soy sauces.

Elucidation of in Vitro Chlorinated Tyrosine Adducts in Blood Plasma as Selective Biomarkers of Chlorine Exposure

Chlorine is a widely available industrial chemical and involved in a substantial number of cases of poisoning. It has also been used as a chemical warfare agent in military conflicts. To enable forensic verification, the persistent biomarkers 3-chlorotyrosine and 3,5-dichlorotyrosine in biomedical samples could be detected. An important shortfall of these biomarkers, however, is the relatively high incidence of elevated levels of chlorinated tyrosine residues in individuals with inflammatory diseases who have not been exposed to chlorine. Therefore, more reliable biomarkers are necessary to distinguish between endogenous formation and exogeneous exposure. The present study aims to develop a novel diagnostic tool for identifying site-specific chlorinated peptides as a more unambiguous indicator of exogeneous chlorine exposure. Human blood plasma was exposed in vitro to various chlorine concentrations, and the plasma proteins were subsequently digested by pronase, trypsin, or pepsin. After sample preparation, the digests were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). In line with other studies, low levels of 3-chlorotyrosine and 3,5-dichlorotyrosine were found in blank plasma samples in this study. Therefore, 50 site-specific biomarkers were identified, which could be used as more unambiguous biomarkers for chlorine exposure. Chlorination of the peptides TY*ETTLEK, Y*KPGQTVK, Y*QQKPGQAPR, HY*EGSTVPEK, and Y*LY*EIAR could already be detected at moderate in vitro chlorine exposure levels. In addition, the latter two peptides were found to have dichlorinated fragments. Especially, Y*LY*EIAR, with a distinct chlorination pattern in the MS spectra, could potentially be used to differentiate exogeneous exposure from endogenous causes as other studies reported that this part of human serum albumin is nitrated rather than chlorinated under physiological conditions. In conclusion, trypsin digestion combined with high-resolution MS analysis of chlorinated peptides could constitute a valuable technique for the forensic verification of exposure to chlorine.

IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.

FAST-IT: Find A Simple Test - In TIA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning-study protocol

INTRODUCTION

Transient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early can prevent subsequent strokes. Recent improvements in mass spectrometer technology allow quantification of hundreds of plasma proteins and lipids, yielding large datasets that would benefit from different approaches including machine learning. Using plasma protein, lipid and radiological biomarkers, our study will develop predictive algorithms to distinguish TIA from minor stroke (positive control) and TIA-mimics (negative control). Analysis including machine learning employs more sophisticated modelling, allowing non-linear interactions, adapting to datasets and enabling development of multiple specialised test-panels for identification and differentiation.

METHODS AND ANALYSIS

Patients attending the Emergency Department, Stroke Ward or TIA Clinic at the Royal Adelaide Hospital with TIA, minor stroke or TIA-like symptoms will be recruited consecutively by staff-alert for this prospective cohort study. Advanced neuroimaging will be performed for each participant, with images assessed independently by up to three expert neurologists. Venous blood samples will be collected within 48 hours of symptom onset. Plasma proteomic and lipid analysis will use advanced mass spectrometry (MS) techniques. Principal component analysis and hierarchical cluster analysis will be performed using MS software. Output files will be analysed for relative biomarker quantitative differences between the three groups. Differences will be assessed by linear regression, one-way analysis of variance, Kruskal-Wallis H-test, χ² test or Fisher's exact test. Machine learning methods will also be applied including deep learning using neural networks.

ETHICS AND DISSEMINATION

Patients will provide written informed consent to participate in this grant-funded study. The Central Adelaide Local Health Network Human Research Ethics Committee approved this study (HREC/18/CALHN/384; R20180618). Findings will be disseminated through peer-reviewed publication and conferences; data will be managed according to our Data Management Plan (DMP2020-00062).

Highly Robust de Novo Full-Length Protein Sequencing

Accurate full-length sequencing of a purified unknown protein is still challenging nowadays due to the error-prone mass-spectrometry (MS)-based methods. De novo identified peptide sequence largely contain errors, undermining the accuracy of assembly. Bias on the detectability of the peptides also makes low-coverage regions, resulting in gaps. Although recent advances on multi-enzyme hydrolysis and algorithms showed complete assembly of full-length protein sequences in a few examples, the robustness in practical application is still to be improved. Here, inspired by genome assembly strategies, we demonstrate a contig-scaffolding strategy to assemble protein sequences with high robustness and accuracy. This strategy integrates multiple unspecific hydrolysis methods to minimize the bias in the hydrolysis process. After de novo identification of the peptides, our assembly algorithm, named Multiple Contigs & Scaffolding (MuCS), assembles the peptide sequences in a multistep, i.e., contig-scaffold manner, with error correction in each step. MS data from different hydrolysis experiments complement each other for robust contig extension and error correction. We demonstrated that our strategy on three proteins and three replications all reached 100% coverage (except one with 98.85%) and 98.69-100% accuracy. It can also efficiently deal with the membrane protein, although the transmembrane region was missing due to the limitation of the MS. The three replicates reached 88.85-92.57% coverage and 97.57-100% accuracy. In sum, we provided a practical, robust, and accurate solution for full-length protein sequencing. The MuCS software is available at http://chi-biotech.com/mucs/.

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 tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction

In this article, we review two challenging computational questions in protein science: neoantigen prediction and protein structure prediction. Both topics have seen significant leaps forward by deep learning within the past five years, which immediately unlocked new developments of drugs and immunotherapies. We show that deep learning models offer unique advantages, such as representation learning and multi-layer architecture, which make them an ideal choice to leverage a huge amount of protein sequence and structure data to address those two problems. We also discuss the impact and future possibilities enabled by those two applications, especially how the data-driven approach by deep learning shall accelerate the progress towards personalized biomedicine.

Next-Generation Serology by Mass Spectrometry: Readout of the SARS-CoV-2 Antibody Repertoire

Methods of antibody detection are used to assess exposure or immunity to a pathogen. Here, we present Ig-MS, a novel serological readout that captures the immunoglobulin (Ig) repertoire at molecular resolution, including entire variable regions in Ig light and heavy chains. Ig-MS uses recent advances in protein mass spectrometry (MS) for multiparametric readout of antibodies, with new metrics like Ion Titer (IT) and Degree of Clonality (DoC) capturing the heterogeneity and relative abundance of individual clones without sequencing of B cells. We applied Ig-MS to plasma from subjects with severe and mild COVID-19 and immunized subjects after two vaccine doses, using the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 as the bait for antibody capture. Importantly, we report a new data type for human serology, that could use other antigens of interest to gauge immune responses to vaccination, pathogens, or autoimmune disorders.

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.

Development and characterization of SARS-CoV-2 variant-neutralizing monoclonal antibodies

Vaccination and administration of monoclonal antibody cocktails are effective tools to control the progression of infectious diseases and to terminate pandemics such as COVID-19. However, the emergence of SARS-CoV-2 mutants with enhanced transmissibility and altered antigenicity requires broad-spectrum therapies. Here we developed a panel of SARS-CoV-2 specific mouse monoclonal antibodies (mAbs), and characterized them based on ELISA, Western immunoblot, isotyping, and virus neutralization. Six neutralizing mAbs that exhibited high-affinity binding to SARS-CoV-2 spike protein were identified, and their amino acid sequences were determined by mass spectrometry. Functional assays confirmed that three mAbs, F461G11, F461G15, and F461G16 neutralized four variants of concern (VOC): B.1.1.7 (alpha), B.1.351 (beta), P.1 (gamma) and B.1.617.2 (delta) These mAbs are promising candidates for COVID-19 therapy, and understanding their interactions with virus spike protein should support further vaccine and antibody development.

A Novel C1q Domain-Containing Protein Isolated from the Mollusk Modiolus kurilensis Recognizing Glycans Enriched with Acidic Galactans and Mannans

C1q domain-containing (C1qDC) proteins are a group of biopolymers involved in immune response as pattern recognition receptors (PRRs) in a lectin-like manner. A new protein MkC1qDC from the hemolymph plasma of Modiolus kurilensis bivalve mollusk widespread in the Northwest Pacific was purified. The isolation procedure included ammonium sulfate precipitation followed by affinity chromatography on pectin-Sepharose. The full-length MkC1qDC sequence was assembled using de novo mass-spectrometry peptide sequencing complemented with N-terminal Edman's degradation, and included 176 amino acid residues with molecular mass of 19 kDa displaying high homology to bivalve C1qDC proteins. MkC1qDC demonstrated antibacterial properties against Gram-negative and Gram-positive strains. MkC1qDC binds to a number of saccharides in Ca2+-dependent manner which characterized by structural meta-similarity in acidic group enrichment of galactose and mannose derivatives incorporated in diversified molecular species of glycans. Alginate, κ-carrageenan, fucoidan, and pectin were found to be highly effective inhibitors of MkC1qDC activity. Yeast mannan, lipopolysaccharide (LPS), peptidoglycan (PGN) and mucin showed an inhibitory effect at concentrations three orders of magnitude greater than for the most effective saccharides. MkC1qDC localized to the mussel hemal system and interstitial compartment. Intriguingly, MkC1qDC was found to suppress proliferation of human adenocarcinoma HeLa cells in a dose-dependent manner, indicating to the biomedical potential of MkC1qDC protein.

Generation and characterisation of a semi-synthetic siderophore-immunogen conjugate and a derivative recombinant triacetylfusarinine C-specific monoclonal antibody with fungal diagnostic application

Invasive pulmonary aspergillosis (IPA) is a severe life-threatening condition. Diagnosis of fungal disease in general, and especially that caused by Aspergillus fumigatus is problematic. A. fumigatus secretes siderophores to acquire iron during infection, which are also essential for virulence. We describe the chemoacetylation of ferrated fusarinine C to diacetylated fusarinine C (DAFC), followed by protein conjugation, which facilitated triacetylfusarinine C (TAFC)-specific monoclonal antibody production with specific recognition of the ferrated form of TAFC. A single monoclonal antibody sequence was ultimately elucidated by a combinatorial strategy involving protein LC-MS/MS, cDNA sequencing and RNAseq. The resultant murine IgG2_a monoclonal antibody was secreted in, and purified from, mammalian cell culture (5 mg) and demonstrated to be highly specific for TAFC detection by competitive ELISA (detection limit: 15 nM) and in a lateral flow test system (detection limit: 3 ng), using gold nanoparticle conjugated- DAFC-bovine serum albumin for competition. Overall, this work reveals for the first time a recombinant TAFC-specific monoclonal antibody with diagnostic potential for IPA diagnosis in traditional and emerging patient groups (e.g., COVID-19) and presents a useful strategy for murine Ig sequence determination, and expression in HEK293 cells, to overcome unexpected limitations associated with aberrant or deficient murine monoclonal antibody production.

Identifying carbohydrate-active enzymes of Cutaneotrichosporon oleaginosus using systems biology

Background

The oleaginous yeast Cutaneotrichosporon oleaginosus represents one of the most promising microbial platforms for resource-efficient and scalable lipid production, with the capacity to accept a wide range of carbohydrates encapsulated in complex biomass waste or lignocellulosic hydrolysates. Currently, data related to molecular aspects of the metabolic utilisation of oligomeric carbohydrates are sparse. In addition, comprehensive proteomic information for C. oleaginosus focusing on carbohydrate metabolism is not available.

Results

In this study, we conducted a systematic analysis of carbohydrate intake and utilisation by C. oleaginosus and investigated the influence of different di- and trisaccharide as carbon sources. Changes in the cellular growth and morphology could be observed, depending on the selected carbon source. The greatest changes in morphology were observed in media containing trehalose. A comprehensive proteomic analysis of secreted, cell wall-associated, and cytoplasmatic proteins was performed, which highlighted differences in the composition and quantity of secreted proteins, when grown on different disaccharides. Based on the proteomic data, we performed a relative quantitative analysis of the identified proteins (using glucose as the reference carbon source) and observed carbohydrate-specific protein distributions. When using cellobiose or lactose as the carbon source, we detected three- and five-fold higher diversity in terms of the respective hydrolases released. Furthermore, the analysis of the secreted enzymes enabled identification of the motif with the consensus sequence LALL[LA]L[LA][LA]AAAAAAA as a potential signal peptide.

Conclusions

Relative quantification of spectral intensities from crude proteomic datasets enabled the identification of new enzymes and provided new insights into protein secretion, as well as the molecular mechanisms of carbo-hydrolases involved in the cleavage of the selected carbon oligomers. These insights can help unlock new substrate sources for C. oleaginosus, such as low-cost by-products containing difficult to utilize carbohydrates. In addition, information regarding the carbo-hydrolytic potential of C. oleaginosus facilitates a more precise engineering approach when using targeted genetic approaches. This information could be used to find new and more cost-effective carbon sources for microbial lipid production by the oleaginous yeast C. oleaginosus.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01692-2.

Deep neural network for detecting arbitrary precision peptide features through attention based segmentation

A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional 'ion mobility' dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.

Progress and challenges in mass spectrometry-based analysis of antibody repertoires

Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution.

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