A step closer to solving the plastics problem
The University of Portsmouth’s Centre for Enzyme Innovation (CEI) researcher, Dr Pattanathu Rahman’s collaborative research brings us a step closer to solving the PETase plastic problem. Dr Rahman discovered a Teesside bacteria called Pseudomonas teessidea in which the plastic eating enzyme PETase can be synthesised.
The challenge is to combine machine learning tools with metabolic modelling to build a genome-scale model to further investigate the eating habits of this bacteria and apply metabolic engineering steps for the overproduction of biosurfactants, which helps to increase enzyme efficiency.
Biosurfactants are a key component of the PETase Solution
Biosurfactants change the properties of the bacterial cell surface by increasing the availability of water repellent molecules; enhancing enzyme activity more than 100 fold against solid PET substances. This means coating surfaces with surfactants can accelerate enzymatic degradation of PET. Dr Pat Rahman explains,
“In the CEI, we prefer to explore the use of biosurfactants due to their lower toxicity and the ease with which they biodegrade compared with surfactants synthesised from petrochemicals.”
Dr Rahman is the lead researcher of the deploy component of the CEI’s transformative research pipeline model and is Director of TeeGene Biotech, a spinout from Teesside University. He is working alongside Dr Claudio Angione, a senior lecturer at Teesside University, on machine learning and metabolic modelling techniques to produce a biosurfactant that will optimise genome-engineered bacteria that contain the plastic eating enzyme, PETase to degrade PET plastics. This research is key to enabling the enzyme to be manufactured and utilised as an industrially commercialised product with application for real-world use.
In April 2018, BBSRC awarded CBMNet over £230,000 from Wave 1 of the Industrial Strategy Challenge Fund (ISCF) to support Early Stage Feasibility Projects, within the strategic scope of the Industrial Biotechnology (IB) Catalyst. Dr Angione and Dr Rahman secured a proof of concept grant through ISCF grant to continue the research.
Computational Modelling and Machine Learning Will Unlock Understanding
The computational methods and machine modelling techniques, combined with the metabolic modelling and biosynthetic engineering, have enabled the researchers to predict and test the growth rates of the bacteria in a variety of conditions. The bacteria which secrete the enzyme can then be optimised to maximise the enzyme’s efficiency. The model uses a variety of techniques to predict reactions and pathways which affect its growth rate. Dr Pattanathu Rahman says,
“The computational predictions from this POC work are excellent and TeeGene would seek further expansion in this technology towards Pseudomonas engineered design for biosurfactants manufacturing and validation of the technology.”
The Future of the Collaborative Project – Next Steps
The ongoing aspect of this project is to integrate machine learning techniques with metabolic modelling to develop an eco-friendly bacteria to support biosurfactant production and ultimately, use it as a tool to solve global environmental issues such as the plastic crisis.
“Integrating machine-learning techniques with genome-scale metabolic models is a highly promising technique, as it has the potential to achieve for the first time interpretable and mechanism-aware machine learning”
reports Dr Claudio Angione, member of the Machine Intelligence Research Group and the Healthcare Innovation Centre at Teesside University.
The CEI is supporting a joint PhD studentship, a Specialist Research Technician and a Research Associate, based at the University of Portsmouth to continue the collaborative project. The long-term goal is the commercialisation of this technology by TeeGene. This project also formed the basis of a submission to the “Sustainable bio-based surfactants for everyone” grand-challenge by Nouryon.
You can read Dr Rahman and Dr Angione’s publication In silico engineering of Pseudomonasmetabolism reveals new biomarkers for increased biosurfactant production