TY - JOUR A1 - Tscheuschner, Georg A1 - Kaiser, Melanie N. A1 - Lisec, Jan A1 - Beslic, D. A1 - Muth, Thilo A1 - Krüger, M. A1 - Mages, H. W. A1 - Dorner, B. G. A1 - Knospe, J. A1 - Schenk, J. A. A1 - Sellrie, F. A1 - Weller, Michael G. T1 - MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour JF - Antibodies N2 - 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. KW - Reproducibility KW - Quality control KW - Traceability KW - Peptides KW - Peptide mass fingerprinting KW - Monoclonal antibody KW - Recombinant antibody KW - Identity KW - Antibody identification KW - Sequencing KW - Light chain KW - Mass spectrometry KW - Software KW - Open science KW - Library KW - COVID-19 KW - Corona virus KW - Sequence coverage KW - NIST-mAb 8671 KW - Reference material KW - RBD KW - Spike protein KW - Nucleocapsid KW - Cleavage KW - Tryptic digest KW - MALDI KW - DHAP KW - 2,5-dihydroxyacetophenone KW - Github KW - Zenodo KW - ABID PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547347 DO - https://doi.org/10.3390/antib11020027 VL - 11 IS - 2 SP - 1 EP - 22 PB - MDPI CY - Basel AN - OPUS4-54734 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beslic, D. A1 - Tscheuschner, Georg A1 - Renard, B. Y. A1 - Weller, Michael G. A1 - Muth, Thilo T1 - Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly JF - Briefings in Bioinformatics N2 - 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. KW - De novo peptide sequencing KW - Bioinformatics KW - Benchmarking study KW - Monoclonal antibody KW - Mass spectrometry KW - Sequence coverage KW - Light chains KW - Heavy chains KW - IgG KW - Immunoglobulins KW - Error correction KW - Sequencing algorithm KW - Preprocessing KW - Missing fragmentation sites KW - Deep learning-based tools PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-570363 DO - https://doi.org/10.1093/bib/bbac542 VL - 24 IS - 1 SP - 1 EP - 12 PB - Oxford University Press AN - OPUS4-57036 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -