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MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour
(2022)
During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used 35 monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied to the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 60 min and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 degrees C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context.
Besides protein identification via mass spectrometric methods, protein and peptide quantification has become more and more important in order to tackle biological questions. Methods like differential gel electrophoresis or enzyme-linked immunosorbent assays have been used to assess protein concentrations, while stable isotope labeling methods are also well established in quantitative proteomics. Recently, we developed metalcoded affinity tagging (MeCAT) as an alternative for accurate and sensitive quantification of peptides and
proteins. In addition to absolute quantification via inductively coupled plasma mass spectrometry, MeCAT also enables sequence analysis via electrospray ionization tandem mass spectrometry. In the current study, we developed a new labeling approach utilizing an iodoacetamide MeCAT reagent (MeCAT-IA). The MeCAT-IA approach shows distinct advantages over the previously used MeCAT with maleinimide reactivity such as higher labelling efficiency and the lack of diastereomer formation during labeling. Here, we present a careful characterization of this new method focusing on the labeling process, which yields complete tagging with an excess of reagent of 1.6 to 1, less complex chromatographic behavior, and fragmentation characteristics of the tagged peptides using the iodoacetamide MeCAT reagent.
Mass spectrometry-based methods play a crucial role in the quantification of the main iron metabolism regulator hepcidin by singling out the bioactive 25-residue peptide from the other naturally occurring N-truncated isoforms (hepcidin-20, -22, -24), which seem to be inactive in iron homeostasis. However, several difficulties arise in the MS analysis of hepcidin due to the sticky character of the peptide and the lack of suitable standards. Here, we propose the use of amino- and fluoro-silanized autosampler vials to reduce hepcidin interaction to laboratory glassware surfaces after testing several types of vials for the preparation of stock solutions and serum samples for isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS). Furthermore, we have investigated two sample preparation strategies and two chromatographic separation conditions with the aim of developing an LC-MS/MS method for the sensitive and reliable quantification of hepcidin-25 in serum samples. A chromatographic separation based on usual acidic mobile phases was compared with a novel approach involving the separation of hepcidin-25 with solvents at high pH containing 0.1% of ammonia. Both methods were applied to clinical samples in an intra-laboratory comparison of two LC-MS/MS methods using the same hepcidin-25 calibrators with good correlation of the results. Finally, we recommend an LC-MS/MS-based quantification method with a dynamic range of 0.5–40 μg/L for the assessment of hepcidin-25 in human serum that uses TFA-based mobile phases and silanized glass vials.
Functional sequences of precision polymers based on thiolactone/Michael chemistry are identified from a large one-bead one-compound library. Single-bead readout by MALDI-TOF MS/MS identifies sequences that host m-THPC that is a second Generation photo-sensitizer drug. The corresponding Tla/Michael-PEG conjugates make m-THPC available in solution and drug payload as well as drug release kinetics can be fine-tuned by the precision segment.
Screening of one-bead-one-compound (OBOC) libraries is a proven procedure for the identification of protein-binding ligands. The demand for binders with high affinity and specificity towards various targets has surged in the biomedical and pharmaceutical field in recent years. The traditional peptide screening involves tedious steps such as affinity selection, bead picking, sequencing, and characterization. Herein, we present a high-throughput “all-on-one chip” system to avoid slow and technically complex bead picking steps. On a traditional glass slide provided with an electrically conductive tape, beads of a combinatorial peptide library are aligned and immobilized by application of a precision sieve. Subsequently, the chip is incubated with a fluorophore-labeled target protein. In a fluorescence scan followed by matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF) mass spectrometry, high-affinity binders are directly and unambiguously sequenced with high accuracy without picking of the positive beads. The use of an optimized ladder sequencing approach improved the accuracy of the de-novo sequencing step to nearly 100%. The new technique was validated by employing a FLAG-based model system, identifying new peptide binders for the monoclonal M2 anti-FLAG antibody, and was finally utilized to search for IgG-binding peptides. In the present format, more than 30,000 beads can be screened on one slide.
Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly
(2023)
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.