The BioCatHub project, applied research data management in biocatalysis

Toxic waste degradation, the (asymmetric) synthesis of active pharmaceutical ingedrients or the carbon-neutral synthesis of chemicals are just some examples in which enzymes are expected to play a key role in the years to come. Publicly available experimental data, produced in scientific processes, is one of the most important assets to realise this. It serves as foundation to reproduce published experimental results or as basis for the development and reviewing of theoretical models. However, to be able to fulfil this role, the produced experimental data must be comprehensible, interoperable, and publicly available, which has often not worked in the past and was even described by its own term: the reproducibility crisis. To counteract the reproducibility crisis, in 2016, the Findable, Accessible, Interoperable and Reusable (FAIR) data principles [1] were published, providing guiding principles in how to collect, document and publish experimental data. Nonetheless, the implementation of research data management (RDM) based on the FAIR data principles is time demanding, requires in depth technical knowledge and therefore often exceeds the capacities of the already busy laboratory staff.

For this purpose, we have developed the BioCatHub project. It aims to provide a RDM infrastructure to solve highly independent and individual RDM challenges in biocatalysis. The central component of the BioCatHub project is the BioCatHub platform. It is created to help experimenters to realise standardised data acquisition based on the FAIR data principles in biocatalysis with four central concepts. First, the BioCatHub platform (retrobiohub.org) works as data collection platform and provides a user interface, helping experimenters to describe experimental procedures in a wizard like structure. Secondly, the BioCatHub platform contains data integration functions to query online databases like SabioRK, Brenda and autocomplete entries for the experimenters during the data collection process. Third, the automated communication between RetroBioCat [2] and the BioCatHub platform enables the planning and seamless integration of data from retrosynthetic reactions. Fourth, the BioCatHub platform writes and reads collected data to and from the machine-readable file format Enzyme markup language (EnzymeML) [3, 4]. This enables the possibility to collect, share, store or publish information about biocatalytic experiments, for example on Zenodo.

On top, for data analysis and processing purposes, we have created the BioCatHub data processing repository, enabling the development of customised and reusable data processing workflows, using data previously collected in the BioCatHub platform as EnzymeML documents. For example, the export of the collected data into a spreadsheet for an experimenter friendly data analysis, the automated estimation of Michaelis-Menten parameters or the description of biocatalytic data using a first order reaction were already implemented as workflows.

The vision realised with the BioCatHub project is to create an extendable infrastructure for researchers in biocatalysis to perform standardised data collection, storage and publishing based on the FAIR data principles combined with reusable data processing workflows adaptable to the needs of experimenters in the lab. Therefore, it should serve for our community and should be extended with the needs of our biotransformation community.

References
1. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. Scientific data. 2016;3:18–27. doi:10.1038/sdata.2016.18
2. Finnigan W, Hepworth LJ, Flitsch SL, Turner NJ. Nature Catalysis. 2021:1–7.
3. Range J, Halupczok C, Lohmann J, Swainston N, Kettner C, Bergmann FT, et al. The FEBS Journal 2021. doi:10.1111/febs.16318
4. Lauterbach, S., Dienhart, H., Range, J. et al. Nat Methods 20, 400–402 (2023). https://doi.org/10.1038