Cooperative biotechnological-chemical design of active washing enzymes and sustainable surfactants

Traditionally, the development of surfactants and enzymes for laundry has been conducted in isolation due to the intricate nature of their interactions, which are only identified at advanced stages of formulation, rendering the process both time-consuming and cost-intensive. This project diverges from conventional methods by optimizing both components concurrently, thereby directly accounting for their mutual influences from the outset. This innovative approach involves the joint development of detergent enzymes and biosurfactants in order to open up new paths to more sustainable detergents.

The research focuses on the optimization of amylases derived from bacterial and fungal sources, tailored specifically for enhanced expression system compatibility through codon usage optimization and further modified with tags to facilitate visualization and purification processes. Enzyme candidates are cloned into an expression vector and subsequently expressed in a fungal system using Aspergillus oryzae for heterologous production. The activity of these amylases is assessed through both quantitative and qualitative assays, carried out in liquid, on culture medium or on textiles.

To navigate the biotechnological development of these enzymes, advanced data analysis techniques are employed. Spectroscopic analyses play a pivotal role in evaluating the combined cleaning efficacy of the enzyme and biosurfactant at the interface within the complex milieu of washing suds. This project aims to refine the performance of enzyme-biosurfactant systems through enzyme engineering, enhancing their properties and ensuring their compatibility with surfactants. Finally, the synergistic biobased enzyme-biosurfactant systems are evaluated on the basis of their washing effect in washing tests.

During the project, energy, water consumption, emissions, and material use are systematically recorded, aiming to establish resource-efficient and hazard-minimized evaluation for applied research projects.