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Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly
(2023)
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.
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.
Mistle: bringing spectral library predictions to metaproteomics with an efficient search index
(2023)
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.
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.
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.
The BAM Data Store
(2023)
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.
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.
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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.
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.
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.
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%.
MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour
(2022)
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.
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.
MALDI-TOF-MS-based identification of monoclonal murine anti-SARS-CoV-2 antibodies within one hour
(2022)
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.
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.
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.
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.
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.
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.
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.
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.