FuturEnzyme: Technologies of the Future for Low-Cost Enzymes for Environment-Friendly Products

FuturEnzyme: Technologies of the Future for Low-Cost Enzymes for Environment-Friendly Products

This H2020 project is a multi-disciplinary and multi-actor consortium of 16 leading academic and industrial partners with the aim to develop technologies of the FUTURe for low-cost ENZYMEs for environment-friendly products. The consortium will produce the most advanced innovative solutions in a fast-track to market platform to discover, design, optimise and formulate enzymes. The high-tech enzyme development platform will use big biodata mining of both public and internal databases and bio-resources, and disruptive machine learning, activity-based bioprospecting, protein engineering, nano-biotechnology, upscale fermentation, and downstream processing systems.

The enzymes developed will be used to obtain economically viable products on 3 market segments, textiles, detergents, and cosmetics, which combine a higher level of functionality with greater sustainability during their production, use or end-of-life: they respond to consumer and industry requests for greener products. We will also bring to fruition the enormous potential of the developed enzymes for use as catalysts to faster yield greener, more valuable, and sustainable products in other market sectors.

The strong collaboration of the academic and industry partners will be supported by clusters, thus making sure we take a holistic approach. Analysing technology, market, consumer, and socio-economic demands, and assessing safety, risk, and life-cycle analysis during the technology and product development in an iterative process, will ensure the results will be effective and relevant. They also contribute to the goals of ensuring innovation capacity and economic success, while reducing the environmental impacts of consumer products. The consortium will interact with a broad stakeholder network to ensure uptake of its developments and feedback insights towards policy-makers.

This project has received funding from the European's Union Horizon 2020 research and innovative programme under grant agreement nº 101000327.