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 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 - CONF A1 - Muth, Thilo T1 - QI-CLOUD Perspektiven zur Entwicklung einer digitalen Qualitätsinfrastruktur N2 - In diesem Vortrag wird die Perspektive einer digitalen Qualitätsinfrastruktur (QI) auf informatischer Seite vorgestellt. Eine zu entwickelnde QI-Cloud ist die Grundlage einer verteilten IT-Plattform über die digitalisierte Prozesse der QI abgewickelt, Daten sicher vorgehalten und ausgetauscht sowie digitale Zertifikate ausgestellt werden können. Dazu werden Methoden wie die Distributed Ledger Technologie sowie Smart Standards beschrieben, die das Potential haben, essentielle technologische Bestandteile einer digital transformierten QI zu werden. T2 - VMPA Tagung CY - Nuremberg, Germany DA - 31.05.2022 KW - Digital quality infrastructure KW - QI-Digital KW - QI-Cloud KW - smart standards PY - 2022 AN - OPUS4-56680 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - Better data quality for man and machine in materials research and testing N2 - Applying data-driven AI systems makes it possible to extract patterns from given data, generate predictions and helps making decisions. Material research and testing holds a plethora of AI-based applications, for example, for the automatized search and synthesis of new materials, the detection of materials defects, or the prediction of process and materials parameters (inverse problems). However, AI algorithms can often only be as good as the training data from which the corresponding models are learned. Therefore, it is also indispensable to develop measures for the standardization and quality assurance of such data. For this purpose, we develop and implement methods from transferring data from various sources into a homogeneous data repository with uniform data descriptions. Through the standardization and corresponding machine-readable interfaces, research data can be made usable and reusable for further data analyses. In addition to the technical implementation of integrative platforms, it is crucial that quality-assured research data management is recognized and implemented as an integral part of daily scientific work. Finally, we provide a vision of how the Federal Institute for Materials Research and Testing can benefit from data-driven AI systems. We discuss early applications and take a peek at future research. T2 - BR50 AI Workshop CY - Online meeting DA - 13.12.2021 KW - Data quality KW - Machine learning KW - Research data management PY - 2021 AN - OPUS4-56681 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - Benchmarking the current state of metaproteomics : a community driven evaluation of experimental and computational techniques N2 - Metaproteomics has substantially grown over the past years and supplements other omics approaches by bringing valuable functional information, enabling genotype- phenotype linkages and connections to metabolic outputs. Currently, a wide variety of metaproteomic workflows is available, yet their impact on the results remains to be thoroughly assessed. Here, we carried out the first community-driven, multi-lab comparison in metaproteomics: the critical assessment of metaproteome investigation (CAMPI) study. Based on well-established workflows, we evaluated the influence of sample preparation, mass spectrometry acquisition, and bioinformatic analysis using two samples: a simplified, lab-assembled human intestinal model and a human fecal sample. Although bioinformatic pipelines contributed to variability in peptide identification, wet-lab workflows were the most important source of differences between analyses. Overall, these peptide-level differences largely disappeared at the protein group level. Differences were observed between peptide- and protein-centric approaches for the predicted community composition but similar functional profiles were found across workflows. The CAMPI findings demonstrate the robustness of current metaproteomics research and provide a perspective for future benchmarking studies. T2 - ISMB/ECCB 2021 CY - Online meeting DA - 28.07.2021 KW - Mass spectrometry KW - Metaproteomics KW - Benchmarking KW - International lab study KW - Bioanalytics PY - 2021 AN - OPUS4-56685 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 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 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 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 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 - Van den Bossche, T. A1 - Verschaffelt, P. A1 - Schallert, K. A1 - Barsnes, H. A1 - Dawyndt, P. A1 - Benndorf, D. A1 - Renard, B. Y. A1 - Mesuere, B. A1 - Martens, L. A1 - Muth, Thilo T1 - Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis N2 - Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the Connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis. Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation. KW - Metaproteomics KW - Bioinformatics KW - Software KW - Pipelines PY - 2020 DO - https://doi.org/10.1021/acs.jproteome.0c00136 VL - 19 IS - 8 SP - 3562 EP - 3566 PB - ACS Publications AN - OPUS4-51331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schiebenhoefer, H. A1 - Schallert, K. A1 - Renard, B. Y. A1 - Trappe, K. A1 - Schmid, E. A1 - Benndorf, D. A1 - Riedel, K. A1 - Muth, Thilo A1 - Fuchs, S. T1 - A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane N2 - Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow. KW - Bioinformatics KW - Protocol KW - Microbial proteomics KW - Software KW - Mass spectrometry KW - Metaproteomics PY - 2020 DO - https://doi.org/10.1038/s41596-020-0368-7 SN - 1750-2799 VL - 15 IS - 10 SP - 3212 EP - 3239 PB - Nature Publishing Group AN - OPUS4-51335 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kuhring, M. A1 - Doellinger, J. A1 - Nitsche, A. A1 - Muth, Thilo A1 - Renard, B. Y. T1 - TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples N2 - Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt. KW - Bioinformatics KW - Mass spectrometry KW - Microbial proteomics KW - Strain identification KW - MS/MS PY - 2020 DO - https://doi.org/10.1021/acs.jproteome.9b00714 VL - 19 IS - 6 SP - 2501 EP - 2510 PB - ACS AN - OPUS4-50942 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Grossegesse, M. A1 - Hartkopf, F. A1 - Nitsche, A. A1 - Schaade, L. A1 - Doellinger, J. A1 - Muth, Thilo T1 - Perspective on Proteomics for Virus Detection in Clinical Samples N2 - One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted. KW - COVID-19 KW - Mass spectrometry KW - Virus diagnostics KW - Virus detection KW - Targeted mass spectrometry KW - Proteomics PY - 2020 DO - https://doi.org/10.1021/acs.jproteome.0c00674 SN - 1535-3907 VL - 19 IS - 11 SP - 4380 EP - 4388 PB - ACS AN - OPUS4-51633 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 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 - Bresch, Harald A1 - Muth, Thilo A1 - Rädler, Jörg A1 - Rühle, Bastian T1 - Datenmanagement im Nanomaterial-Labor: Einsatz von ELN-Software zur digitalen Transformation des Laboralltags N2 - Angesichts der zunehmenden Digitalisierung und dem Einsatz datenintensiver Methodiken in der Wissenschaft stehen Forschende vor der Herausforderung, stetig wachsende Datenmengen nachvollziehbar zu dokumentieren, langfristig zu speichern und für Dritte nachnutzbar zu machen. Um diesen Anforderungen gerecht zu werden, bietet sich die Nutzung von Software-Lösungen an, welche Forschungsdatenmanagement mit der digitalen Dokumentation von Laborinventar und Experimenten in elektronischen Laborbüchern (engl. electronic lab notebooks (ELN)) verknüpfen. KW - NanoPlattform KW - BAM Data Store KW - OpenBIS KW - Forschungsdatenmanagement KW - Research data management KW - Elektronisches Laborbuch KW - Electronic lab notebook (ELN) PY - 2023 UR - https://analyticalscience.wiley.com/content/article-do/datenmanagement-im-nanomaterial-labor SN - 0016-3538 IS - 9 SP - 24 EP - 26 PB - Wiley Analytical Science CY - Weinheim AN - OPUS4-59600 LA - deu 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 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 - CONF A1 - Portella, Pedro Dolabella A1 - Skrotzki, Birgit A1 - Muth, Thilo T1 - Die Digitalisierung der Materialwissenschaft und Werkstofftechnik - ein Überblick N2 - Die Bereitstellung von zuverlässigen Werkstoffdaten stand für die Werkstoffprüfung seit dem 19. Jahrhundert in zentraler Stelle. Mit der zunehmender Digitalisierung unserer Gesellschaft gewinnt das Datenmanagement insbesondere im Hinblick auf die Modellierungs- und Simulationsaktivitäten an Bedeutung. In unserem Beitrag beschreiben wir die gegenwärtigen Aktivitäten in Deutschland – insbesondere die NFDI-MatWerk und die BMBF-Initiative MaterialDigital – sowie in Europa und Übersee. Abschließend heben wir die Auswirkungen auf die technische und akademische Aus- und Weiterbildung. T2 - DVM - Tagung Werkstoffprüfung CY - Online meeting DA - 03.12.2020 KW - Werkstoffdaten KW - Digitalisierung KW - Materialwissenschaft PY - 2020 AN - OPUS4-52044 LA - deu 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 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 - TY - JOUR A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores N2 - Motivation: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. Results: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. Availability and implementation: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM. KW - Graphical model KW - Protemics KW - Virus KW - Ayesian models PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-577488 SN - 1367-4803 VL - 39 IS - 5 SP - 1 EP - 11 PB - Oxford University Press CY - Oxford, UK AN - OPUS4-57748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nowatzky, Yannek A1 - Benner, Philipp A1 - Reinert, K. A1 - Muth, Thilo T1 - Mistle: bringing spectral library predictions to metaproteomics with an efficient search index N2 - Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Availability and implementation: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle. KW - Mass spectrometry KW - Proteomics KW - Algorithms KW - Metaproteomics PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-579373 DO - https://doi.org/10.1093/bioinformatics/btad376 SN - 1367-4811 VL - 39 IS - 6 SP - 1 EP - 12 PB - Oxford University Press CY - Oxford, Great Britain AN - OPUS4-57937 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Valdestilhas, Andre A1 - Bayerlein, Bernd A1 - Moreno Torres, Benjamin A1 - Jan Zia, Ghezal Ahmad A1 - Muth, Thilo T1 - The Intersection Between Semantic Web and Materials Science N2 - The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE. KW - Linked open data KW - Materials science KW - Ontology KW - Semantic web PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575506 DO - https://doi.org/10.1002/aisy.202300051 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-57550 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Monavari, Mehran A1 - Prellwitz, Matthias A1 - Muth, Thilo A1 - Eichstädt, Sascha A1 - Koch, Claudia T1 - Quality-X: A Federated Digital Ecosystem for the Future Quality Infrastructure N2 - Harmonized and interoperable national Quality Infrastructure (QI) systems are essential for fostering cooperation, promoting mutual trust, and facilitating trade. The true potential of the QI is realized when its elements and actors are seamlessly integrated into a cohesive digital QI ecosystem. Recent developments towards industrial international data spaces enable such an ecosystem but require the integration of QI principles. Recognizing the lack of such a platform, Quality-X aims at setting the stage for the implementation of a QI ecosystem in international data spaces (IDS), GAIA-X and related German and European projects dedicated to secure data sharing. Quality-X is not about the construction of a platform; it is the creation of an inclusive QI ecosystem with harmonized interfaces. Instead of imposing rigid data structures, it prioritizes interoperability. Through the utilization of Decentralized Identifiers (DIDs), Verifiable Credentials, and Identity Hubs, Quality-X seeks seamless interactions across diverse service provider systems. This white paper introduces the concept and vision of Quality-X and discusses the general prerequisites for integrating QI processes within data spaces. Further on, we introduce existing testbeds, which will serve as an experimental proving ground for exploring various use cases related to the implementation of the vision of a QI-Digital. +++ Harmonisierte und interoperable nationale Qualitätsinfrastrukturen (QI) sind für die Förderung der Zusammenarbeit, des gegenseitigen Vertrauens und der Erleichterung des Handels unerlässlich. Das wahre Potenzial der QI kommt zum Tragen, wenn ihre Elemente und Akteure nahtlos in ein kohärentes digitales QI-Ökosystem integriert werden. Die jüngsten Entwicklungen hin zu industriellen internationalen Datenräumen ermöglichen ein solches Ökosystem, erfordern jedoch die Integration von QI-Prinzipien. Angesichts des Fehlens einer solchen Plattform zielt Quality-X darauf ab, die Voraussetzungen für die Umsetzung eines QI-Ökosystems in internationalen Datenräumen (IDS), GAIA-X und verwandten deutschen und europäischen Projekten zum sicheren Datenaustausch zu schaffen. Bei Quality-X geht es nicht um den Aufbau einer Plattform, sondern um die Schaffung eines umfassenden QI-Ökosystems mit harmonisierten Schnittstellen. Anstatt starre Datenstrukturen aufzuerlegen, steht die Interoperabilität im Vordergrund. Durch die Verwendung von dezentralen Identifikatoren (DIDs), überprüfbaren Berechtigungsnachweisen und Identitäts-Hubs strebt Quality-X eine nahtlose Interaktion zwischen verschiedenen Systemen von Dienstleistern an. Dieses Whitepaper stellt das Konzept und die Vision von Quality-X vor und erörtert die allgemeinen Voraussetzungen für die Integration von QI-Prozessen in Datenräumen. Darüber hinaus stellen wir bestehende Testbeds vor, die als experimentelles Versuchsfeld für die Erforschung verschiedener Anwendungsfälle im Zusammenhang mit der Umsetzung der Vision einer QI-Digital dienen sollen. KW - Quality Assurance KW - Quality Infrastructure KW - Data Spaces KW - Digitalization KW - Quality Management KW - Interoperability KW - Verification KW - Identification KW - Certificate KW - Datenräume KW - Qualitätsinfrastruktur KW - Gaia-X KW - Manufacturing-X KW - Catena-X KW - Datenökosystem KW - Qualitätssicherung KW - Qualitätsmanagement PY - 2023 UR - https://www.qi-digital.de/fileadmin/user_upload/website/publikationen/1022_Brosch%C3%BCre_Quality-X_v4.pdf SP - 1 CY - Berlin AN - OPUS4-59354 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Arikan, Muzaffer A1 - Muth, Thilo T1 - Integrated multi-omics analyses of microbial communities: A review of the current state and future directions N2 - Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions. KW - Microbiome KW - Multi-omics KW - Data integration PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580785 DO - https://doi.org/10.1039/d3mo00089c SP - 1 EP - 17 PB - Royal Society of Chemistry AN - OPUS4-58078 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Walke, D. A1 - Micheel, D. A1 - Schallert, K. A1 - Muth, Thilo A1 - Broneske, D. A1 - Saake, G. A1 - Heyer, R. T1 - The importance of graph databases and graph learning for clinical applications N2 - The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. KW - Graph databases KW - Graph learning KW - Review KW - RDF PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580858 DO - https://doi.org/10.1093/database/baad045 SN - 1758-0463 SP - 1 EP - 20 AN - OPUS4-58085 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Muth, Thilo T1 - MassIVE MSV000089620 - Critical Assessment of MetaProteome Investigation (CAMPI): A multi-lab comparison of established workflows N2 - Metaproteomics, the study of the collective proteome within a microbial ecosystem, has substantially grown over the past few years. This growth comes from the increased awareness that it can powerfully supplement metagenomics and metatranscriptomics analyses. Although metaproteomics is more challenging than single-species proteomics, its added value has already been demonstrated in various biosystems, such as gut microbiomes or biogas plants. Because of the many challenges, a variety of metaproteomics workflows have been developed, yet it remains unclear what the impact of the choice of workflow is on the obtained results. Therefore, we set out to compare several well-established workflows in the first community-driven, multi-lab comparison in metaproteomics: the critical assessment of metaproteome investigation (CAMPI) study. In this benchmarking study, we evaluated the influence of different workflows on sample preparation, mass spectrometry acquisition, and bioinformatic analysis on two samples: a simplified, lab-assembled human intestinal sample and a complex human fecal sample. We find that the same overall biological meaning can be inferred from the metaproteome data, regardless of the chosen workflow. Indeed, taxonomic and functional annotations were very similar across all sample-specific data sets. Moreover, this outcome was consistent regardless of whether protein groups or peptides, or differences at the spectrum or peptide level were used to infer these annotations. Where differences were observed, those originated primarily from different wet-lab methods rather than from different bioinformatic pipelines. The CAMPI study thus provides a solid foundation for benchmarking metaproteomics workflows, and will therefore be a key reference for future method improvement. [doi:10.25345/C5SX64D9M] [dataset license: CC0 1.0 Universal (CC0 1.0)] KW - Benchmarking KW - Data set PY - 2021 DO - https://doi.org/10.25345/c5sx64d9m PB - University of California CY - San Diego AN - OPUS4-58087 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - The promise of mass spectrometry-based virus proteomics: taking a peek at current bioinformatics applications and limitations N2 - Driven by recent technological advances and the need for improved viral diagnostic applications, mass spectrometry-based proteomics comes into play for detecting viral pathogens accurately and efficiently. However, the lack of specific algorithms and software tools presents a major bottleneck for analyzing data from host-virus samples. For example, accurate species- and strain-level classification of a priori unidentified organisms remains a very challenging task in the setting of large search databases. Another prominent issue is that many existing solutions suffer from the protein inference issue, aggravated because many homologous proteins are present across multiple species. One of the contributing factors is that existing bioinformatic algorithms have been developed mainly for single-species proteomics applications for model organisms or human samples. In addition, a statistically sound framework was lacking to accurately assign peptide identifications to viral taxa. In this presentation, an overview is given on current bioinformatics developments that aim to overcome the above-mentioned issues using algorithmic and statistical methods. The presented methods and software tools aim to provide tailored solutions for both discovery-driven and targeted proteomics for viral diagnostics and taxonomic sample profiling. Furthermore, an outlook is provided on how the bioinformatic developments might serve as a generic toolbox, which can be transferred to other research questions, such as metaproteomics for profiling microbiomes and identifying bacterial pathogens. T2 - European Virus Bioinformatics Center in-silico lecture series CY - Online meeting DA - 28.02.2022 KW - Mass spectrometry KW - Data science KW - Virus detection KW - Bioinformatic algorithms PY - 2022 AN - OPUS4-56682 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 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 - 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 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 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 - TY - JOUR A1 - Albakri, Bashar A1 - Turski Silva Diniz, Analice A1 - Benner, Philipp A1 - Muth, Thilo A1 - Nakajima, Shinichi A1 - Favaro, Marco A1 - Kister, Alexander ED - Hillman, Robert T1 - Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers N2 - Polymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates a convolutional neural network (CNN) model to predict the EEC topology. By reducing user bias and enhancing data analysis, this approach aims to make BDS accessible to both experienced users and those with limited expertise. We show that the combination of machine learning and BDS provides valuable insights into the dynamic behaviour of polymer membranes, thus facilitating the design and characterization of tailored polymers for various applications. We also show that our model outperforms state-of-the-art machine learning methods with a top-5 accuracy of around 80% for predicting the circuit topology and a parameter fitting error as low as 0.05%. KW - Polymer membranes KW - Electrochemical impedance spectroscopy KW - Broadband dielectric spectroscopy KW - Deep learning KW - Machine learning KW - Equivalent circuit PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602138 DO - https://doi.org/10.1016/j.electacta.2024.144474 SN - 0013-4686 VL - 496 SP - 1 EP - 13 PB - Elsevier Ltd. AN - OPUS4-60213 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -