Application
of marine-derived enzymes in the degradation of plastic and beneficiation
of alginate
Current AMHBI Research Projects (Part 4 of 6) Contacts:
A/Prof Marilize Le Roes-Hill (LeRoesM@cput.ac.za); ORCiD: 0000-0002-1930-2637
Dr Alaric Prins (PrinsAl@cput.ac.za); ORCiD: 0000-0002-1797-3648
CPUT Research Focus Area: Bioeconomy and Biotechnology
CPUT Research Niche Areas: Biocatalysis and Enzyme Production
EnzymeML
(https://enzymeml.org/)
This National Research Foundation-funded project is focused on exploring the ability of marine-derived enzymes, specifically multicopper oxidases, to degrade different types of microplastics found in the environment, as well as their application to act in synergy with alginate lyases in the beneficiation of seaweed (source of alginate), the most abundant marine carbon source. This multidisciplinary project is not only focused on the generation of products with potential health benefits, but will also be exploring the use of alginate for biofuel production, while making use of EnzymeML for data captured during the course of the research study.
PyEED (https://github.com/PyEED)
This is another consortium-based project driven by the University of Stuttgart, with CPUT as one of the international partners. The project is aimed at the development of Python-based approaches for the development of enzyme databases. PyEED-jupyternotebook is one such example where a pipeline for the analysis of sequence data allows for the storage of the data in a database, which can be expanded on as more information becomes available or it can be used for the analysis of specific sequence and structural properties for the design of improved biocatalysts.
This consortium-based project is driven by the University of Stuttgart, with CPUT as one of the international partners. Reproducibility, replicability, and repeatability of enzyme kinetic parameter estimations are large problems in the field of enzymology, with more than 70% of researchers not being able to reproduce the results of another researcher, and up to 50% unable to reproduce the outcomes of their own data. EnzymeML was designed to enable the data exchange of biocatalysis and biochemical data in a standardised format, prepared according to STRENDA guidelines and obeying the FAIR principals. This ensure that the data generated is Findable, Accessible, Interoperable and Reproducible. The EnzymeML data format, therefore, ensures that raw enzymatic data is accompanied by metadata that thoroughly documents all reaction conditions, as well as any modelling parameters utilised in a said experiment or set of experiments.
