TY - JOUR A1 - Celasun, S. A1 - Remmler, D. A1 - Schwaar, Timm A1 - Weller, Michael G. A1 - Du Prez, F. A1 - Börner, H. G. T1 - Digging into the sequential space of thiolactone precision polymers: A combinatorial strategy to identify functional domains JF - Angew. Chem. Int. Ed. N2 - 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. KW - Combinatorial chemistry KW - Combinatorial polymer libraries KW - Sequence-defined oligomer KW - Precision polymer sequencing KW - Pseudo peptides KW - MALDI-TOF KW - ESI MS KW - Mass spectrometry KW - Sequencing KW - PEG KW - Polyethylene glycol KW - Solubilizer KW - Drug KW - Conjugates PY - 2019 DO - https://doi.org/10.1002/anie.201810393 SN - 1521-3773 VL - 58 IS - 7 SP - 1960 EP - 1964 PB - Wiley-VCH CY - Weinheim AN - OPUS4-47323 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 -