TY - JOUR A1 - Altenburg, T. A1 - Giese, S. A1 - Wang, S. A1 - Muth, Thilo A1 - Renard, B.Y. T1 - Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides JF - Nature Machine Intelligence N2 - 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%. KW - Mass spectrometry KW - Machine learning KW - Deep learning KW - Peptide identification PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547580 DO - https://doi.org/10.1038/s42256-022-00467-7 SN - 2522-5839 VL - 4 SP - 378 EP - 388 PB - Springer Nature CY - London AN - OPUS4-54758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - GEN 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 T2 - Preprints 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 thirty-five monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied onto 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 45 minutes 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 °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 (https://gets.shinyapps.io/ABID/). 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 - SARS-CoV-2 antibody KW - Reproducibility crisis KW - Peptide mass fingerprinting KW - Monoclonal antibody KW - Traceability KW - Antibody identification KW - Identity KW - Antibody light chain KW - MALDI-TOF-MS KW - Trypsin KW - Acidic cleavage KW - Antibody subclass KW - Database KW - Peak overlap KW - ABID KW - Sulfuric acid KW - Online software KW - Sequencing KW - Peptide coverage PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-545182 DO - https://doi.org/10.20944/preprints202203.0229.v1 SN - 2310-287X SP - 1 EP - 24 PB - MDPI CY - Basel AN - OPUS4-54518 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Van Den Bossche, T. A1 - Arntzen, M. O. A1 - Becher, D. A1 - Benndorf, D. A1 - Eijsink, V. G. H. A1 - Henry, C. A1 - Jagtap, P. D. A1 - Jehmlich, N. A1 - Juste, C. A1 - Kunath, B. J. A1 - Mesuere, B. A1 - Muth, Thilo A1 - Pope, P. B. A1 - Seifert, J. A1 - Tanca, A. A1 - Uzzau, S. A1 - Wilmes, P. A1 - Hettich, R. L. A1 - Armengaud, J. T1 - The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes JF - Microbiome N2 - Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this feld. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics. KW - Microbiome KW - Metaproteomics KW - Networking KW - Meta-Omics KW - Interactions KW - Education PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542290 DO - https://doi.org/10.1186/s40168-021-01176-w VL - 9 IS - 1 SP - 243 PB - BMC AN - OPUS4-54229 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Van Den Bossche, T. A1 - Kunath, B. A1 - Schallert, K. A1 - Schäpe, S. A1 - Abraham, P. E. A1 - Armengaud, J. A1 - Arntzen, M. Ø. A1 - Bassignani, A. A1 - Benndorf, D. A1 - Fuchs, S. A1 - Giannone, R. J. A1 - Griffin, T. J. A1 - Hagen, L. H. A1 - Halder, R. A1 - Henry, C. A1 - Hettich, R. L. A1 - Heyer, R. A1 - Jagtap, P. A1 - Jehmlich, N. A1 - Jensen, M. A1 - Juste, C. A1 - Kleiner, M. A1 - Langella, O. A1 - Lehmann, T. A1 - Leith, E. A1 - May, P. A1 - Mesuere, B. A1 - Miotello, G. A1 - Peters, S. L. A1 - Pible, O. A1 - Queiros, P. T. A1 - Reichl, U. A1 - Renard, B. Y. A1 - Schiebenhoefer, H. A1 - Sczyrba, A. A1 - Tanca, A. A1 - Trappe, K. A1 - Trezzi, J.-P. A1 - Uzzau, S. A1 - Verschaffelt, P. A1 - von Bergen, M. A1 - Wilmes, P. A1 - Wolf, M. A1 - Martens, L. A1 - Muth, Thilo T1 - Critical Assessment of MetaProteome Investigation (CAMPI): A multi-laboratory comparison of established workflows JF - Nature communications N2 - Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments. KW - Metaproteomics KW - Mass spectrometry KW - Data science KW - Benchmarking KW - Bioinformatics PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-541220 DO - https://doi.org/10.1038/s41467-021-27542-8 SN - 2041-1723 VL - 12 SP - 1 EP - 15 PB - Nature Publishing Group CY - London AN - OPUS4-54122 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Anzt, H. A1 - Bach, F. A1 - Druskat, S. A1 - Löffler, F. A1 - Loewe, A. A1 - Renard, B. Y. A1 - Seemann, G. A1 - Struck, A. A1 - Achhammer, E. A1 - Aggarwal, P. A1 - Appel, F. A1 - Bader, M. A1 - Brusch, L. A1 - Busse, C. A1 - Chourdakis, G. A1 - Dabrowski, P. W. A1 - Ebert, P. A1 - Flemisch, B. A1 - Friedl, S. A1 - Fritzsch, B. A1 - Funk, M. D. A1 - Gast, V. A1 - Goth, F. A1 - Grad, J. A1 - Hegewald, J. A1 - Hermann, S. A1 - Hohmann, F. A1 - Janosch, S. A1 - Kutra, D. A1 - Linxweiler, J. A1 - Muth, Thilo A1 - Peters-Kottig, W. A1 - Rack, F. A1 - Raters, F. H. C. A1 - Rave, S. A1 - Reina, G. A1 - Reißig, M. A1 - Ropinski, T. A1 - Schaarschmidt, J. A1 - Seibold, H. A1 - Thiele, J. P. A1 - Uekermann, B. A1 - Unger, S. A1 - Weeber, R. T1 - An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action JF - F1000 Research N2 - Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. KW - Research Software KW - Sustainable Software Development KW - Academic Software KW - Software Infrastructure KW - Software Training KW - Software Licensing PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-522029 DO - https://doi.org/10.12688/f1000research.23224.2 VL - 9 SP - 1 EP - 35 AN - OPUS4-52202 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pech, S. A1 - Rehberg, M. A1 - Janke, R. A1 - Benndorf, D. A1 - Genzel, Y. A1 - Muth, Thilo A1 - Sickmann, A. A1 - Rapp, E. A1 - Reichl, U. T1 - Tracking changes in adaptation to suspension growth for MDCK cells: cell growth correlates with levels of metabolites, enzymes and proteins JF - Applied Microbiology and Biotechnology N2 - Adaptations of animal cells to growth in suspension culture concern in particular viral vaccine production, where very specific aspects of virus-host cell interaction need to be taken into account to achieve high cell specific yields and overall process productivity. So far, the complexity of alterations on the metabolism, enzyme, and proteome level required for adaptation is only poorly understood. In this study, for the first time, we combined several complex analytical approaches with the aim to track cellular changes on different levels and to unravel interconnections and correlations. Therefore, a Madin-Darby canine kidney (MDCK) suspension cell line, adapted earlier to growth in suspension, was cultivated in a 1-L bioreactor. Cell concentrations and cell volumes, extracellular metabolite concentrations, and intracellular enzyme activities were determined. The experimental data set was used as the input for a segregated growth model that was already applied to describe the growth dynamics of the parental adherent cell line. In addition, the cellular proteome was analyzed by liquid chromatography coupled to tandem mass spectrometry using a label-free protein quantification method to unravel altered cellular processes for the suspension and the adherent cell line. Four regulatory mechanisms were identified as a response of the adaptation of adherent MDCK cells to growth in suspension. These regulatory mechanisms were linked to the proteins caveolin, cadherin-1, and pirin. Combining cell, metabolite, enzyme, and protein measurements with mathematical modeling generated a more holistic view on cellular processes involved in the adaptation of an adherent cell line to suspension growth. KW - MDCK cell KW - Proteome KW - Metabolism KW - Enzyme activity KW - Suspension growth PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-522070 DO - https://doi.org/10.1007/s00253-021-11150-z VL - 105 IS - 5 SP - 1861 EP - 1874 PB - Springer AN - OPUS4-52207 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sajulga, R. A1 - Easterly, C. A1 - Riffle, M. A1 - Mesuere, B. A1 - Muth, Thilo A1 - Mehta, S. A1 - Kumar, P. A1 - Johnson, J. A1 - Gruening, B. A1 - Schiebenhoefer, H. A1 - Kolmeder, C. A1 - Fuchs, S. A1 - Nunn, B. A1 - Rudney, J. A1 - Griffin, T. A1 - Jagtap, P. T1 - Survey of metaproteomics software tools for functional microbiome analysis JF - PLoS ONE N2 - To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform. KW - Bioinformatics KW - Metaproteomics KW - Mass spectrometry PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-516358 DO - https://doi.org/10.1371/journal.pone.0241503 SP - e0241503 AN - OPUS4-51635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Athman, Rukeia A1 - Rädler, Jörg A1 - Löhmann, Oliver A1 - Ariza, Angela A1 - Muth, Thilo T1 - The BAM Data Store JF - Proceedings of the Conference on Research Data Infrastructure N2 - As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE. T2 - 1st Conference on Research Data Infrastructure DA - 12.09.2023 KW - Research Data Infrastructure KW - Electronic Lab Notebook (ELN) KW - openBIS KW - Research Data Management PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596032 DO - https://doi.org/10.52825/CoRDI.v1i.229 VL - 1 SP - 1 EP - 5 AN - OPUS4-59603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering JF - Advanced Engineering Materials N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -