03 Fakultät Chemie

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/4

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Author: search.filters.author.Pleiss, JürgenSubject: search.filters.subject.004

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    Visual analysis of large‐scale protein‐ligand interaction data
    (2021) Schatz, Karsten; Franco‐Moreno, Juan José; Schäfer, Marco; Rose, Alexander S.; Ferrario, Valerio; Pleiss, Jürgen; Vázquez, Pere‐Pau; Ertl, Thomas; Krone, Michael
    When studying protein‐ligand interactions, many different factors can influence the behaviour of the protein as well as the ligands. Molecular visualisation tools typically concentrate on the movement of single ligand molecules; however, viewing only one molecule can merely provide a hint of the overall behaviour of the system. To tackle this issue, we do not focus on the visualisation of the local actions of individual ligand molecules but on the influence of a protein and their overall movement. Since the simulations required to study these problems can have millions of time steps, our presented system decouples visualisation and data preprocessing: our preprocessing pipeline aggregates the movement of ligand molecules relative to a receptor protein. For data analysis, we present a web‐based visualisation application that combines multiple linked 2D and 3D views that display the previously calculated data The central view, a novel enhanced sequence diagram that shows the calculated values, is linked to a traditional surface visualisation of the protein. This results in an interactive visualisation that is independent of the size of the underlying data, since the memory footprint of the aggregated data for visualisation is constant and very low, even if the raw input consisted of several terabytes.
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    EnzymeML : a data exchange format for biocatalysis and enzymology
    (2021) Range, Jan; Halupczok, Colin; Lohmann, Jens; Swainston, Neil; Kettner, Carsten; Bergmann, Frank T.; Weidemann, Andreas; Wittig, Ulrike; Schnell, Santiago; Pleiss, Jürgen
    EnzymeML is an XML‐based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO‐RK.