Universität Stuttgart

Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1

Browse

Filters

2021 - 20245

Settings

Author: search.filters.author.Pleiss, JürgenSubject: search.filters.subject.660

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen Access
    Fluent integration of laboratory data into biocatalytic process simulation using EnzymeML, DWSIM, and ontologies
    (2024) Behr, Alexander S.; Surkamp, Julia; Abbaspour, Elnaz; Häußler, Max; Lütz, Stephan; Pleiss, Jürgen; Kockmann, Norbert; Rosenthal, Katrin
    The importance of biocatalysis for ecologically sustainable syntheses in the chemical industry and for applications in everyday life is increasing. To design efficient applications, it is important to know the related enzyme kinetics; however, the measurement is laborious and error-prone. Flow reactors are suitable for rapid reaction parameter screening; here, a novel workflow is proposed including digital image processing (DIP) for the quantification of product concentrations, and the use of structured data acquisition with EnzymeML spreadsheets combined with ontology-based semantic information, leading to rapid and smooth data integration into a simulation tool for kinetics evaluation. One of the major findings is that a flexibly adaptive ontology is essential for FAIR (findability, accessibility, interoperability, reusability) data handling. Further, Python interfaces enable consistent data transfer.
  • Thumbnail Image
    ItemOpen Access
    Standardized data, scalable documentation, sustainable storage : EnzymeML ss a basis for FAIR data management in biocatalysis
    (2021) Pleiss, Jürgen
    The often reported reproducibility crisis in the biomedical sciences also applies to enzymology and biocatalysis, and mainly results from incomplete reporting of reaction conditions. In this Concept article, an infrastructure based on EnzymeML is sketched, which enables reporting, exchange, and storage of enzymatic data according to the FAIR data principles. EnzymeML is a novel data exchange format for enzymology and biocatalysis, which facilitates the application of the STRENDA Guidelines and thus makes data on enzyme‐catalyzed reactions findable, accessible, interoperable, and reusable. EnzymeML enables the comprehensive documentation of metadata, thus fostering reproducibility and replicability in enzymology and biocatalysis. An EnzymeML Application Programming Interface integrates electronic lab notebooks with modelling platforms and databases on enzymatic reactions, and thus enables the seamless flow of enzymatic data from measurement to modelling to publication, without the need for manual intervention such as reformatting or editing. EnzymeML serves as a valuable tool for the design of biocatalytic experiments and contributes to the vision of a unified research data infrastructure for catalysis research.
  • Thumbnail Image
    ItemOpen Access
    Inverting the stereoselectivity of an NADH‐dependent imine‐reductase variant
    (2021) Stockinger, Peter; Borlinghaus, Niels; Sharma, Mahima; Aberle, Benjamin; Grogan, Gideon; Pleiss, Jürgen; Nestl, Bettina M.
    Imine reductases (IREDs) offer biocatalytic routes to chiral amines and have a natural preference for the NADPH cofactor. In previous work, we reported enzyme engineering of the (R)‐selective IRED from Myxococcus stipitatus (NADH‐IRED‐Ms) yielding a NADH‐dependent variant with high catalytic efficiency. However, no IRED with NADH specificity and (S)‐selectivity in asymmetric reductions has yet been reported. Herein, we applied semi‐rational enzyme engineering to switch the selectivity of NADH‐IRED‐Ms. The quintuple variant A241V/H242Y/N243D/V244Y/A245L showed reverse stereopreference in the reduction of the cyclic imine 2‐methylpyrroline compared to the wild‐type and afforded the (S)‐amine product with >99 % conversion and 91 % enantiomeric excess. We also report the crystal‐structures of the NADPH‐dependent (R)‐IRED‐Ms wild‐type enzyme and the NADH‐dependent NADH‐IRED‐Ms variant and molecular dynamics (MD) simulations to rationalize the inverted stereoselectivity of the quintuple variant.
  • Thumbnail Image
    ItemOpen Access
    MetaConfigurator : a user-friendly tool for editing structured data files
    (2024) Neubauer, Felix; Bredl, Paul; Xu, Minye; Patel, Keyuriben; Pleiss, Jürgen; Uekermann, Benjamin
    Textual formats to structure data, such as JSON, XML, and YAML, are widely used for structuring data in various domains, from configuration files to research data. However, manually editing data in these formats can be complex and time-consuming. Graphical user interfaces (GUIs) can significantly reduce manual efforts and assist the user in editing the files, but developing a file-format-specific GUI requires substantial development and maintenance efforts. To address this challenge, we introduce MetaConfigurator : an open-source web application that generates its GUI depending on a given schema. Our approach differs from other schema-to-UI approaches in three key ways: 1) It offers a unified view that combines the benefits of both GUIs and text editors, 2) it enables schema editing within the same tool, and 3) it supports advanced schema features, including conditions and constraints. In this paper, we discuss the design and implementation of MetaConfigurator , backed by insights from a small-scale qualitative user study. The results indicate the effectiveness of our approach in retrieving information from data and schemas and in editing them.
  • Thumbnail Image
    ItemOpen Access
    Research data management in simulation science : infrastructure, tools, and applications
    (2024) Flemisch, Bernd; Hermann, Sibylle; Herschel, Melanie; Pflüger, Dirk; Pleiss, Jürgen; Range, Jan; Roy, Sarbani; Takamoto, Makoto; Uekermann, Benjamin
    Research Data Management (RDM) has gained significant traction in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech). After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques.