@article{TscheuschnerKaiserLisecetal.2022, author = {Tscheuschner, Georg and Kaiser, Melanie N. and Lisec, Jan and Beslic, Denis and Muth, Thilo and Kr{\"u}ger, Maren and Mages, Hans Werner and Dorner, Brigitte G. and Knospe, Julia and Schenk, J{\"o}rg A. and Sellrie, Frank and Weller, Michael G.}, title = {MALDI-TOF-MS-based identification of monoclonal murine Anti-SARS-CoV-2 antibodies within one hour}, series = {Antibodies}, volume = {11}, journal = {Antibodies}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2073-4468}, doi = {10.3390/antib11020027}, pages = {22}, year = {2022}, abstract = {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.}, language = {en} } @article{AltenburgGieseWangetal.2022, author = {Altenburg, Tom and Giese, Sven Hans-Joachim and Wang, Shengbo and Muth, Thilo and Renard, Bernhard Y.}, title = {Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides}, series = {Nature machine intelligence}, volume = {4}, journal = {Nature machine intelligence}, number = {4}, publisher = {Springer Nature Publishing}, address = {London}, issn = {2522-5839}, doi = {10.1038/s42256-022-00467-7}, pages = {378 -- 388}, year = {2022}, abstract = {Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra. Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce 'ad hoc learning of fragmentation' (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4\% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1\% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94\%.}, language = {en} }