Researchers from BASF, the Austrian Research Centre of Industrial Biotechnology (acib) and the University of Graz in Austria have co-developed a new computer-assisted model that reportedly can improve enzyme performance and enable new biocatalytic production processes to be scaled up faster from the lab to industrial manufacturing.
Enzymes, biological catalysts that speed up chemical reactions, are used as biocatalysts in production processes. BASF uses enzymes to make products such as vitamins, flavorings or ingredients for cosmetics and detergents.
However, enzymes are sensitive and stop working properly if, for example, the temperature is too high. They also can’t function optimally when temperatures are too low, producing lower volumes of the desired product. Substances contained in the reaction medium, such as solvents, can also influence the activity of the biocatalysts.
To get the largest yield of the desired product, the optimal point for the enzyme must be found where both the reaction temperature and the solvent concentration result in the highest possible activity. This is a complex process involving many laboratory experiments.
Researchers from BASF, acib and the University of Graz have now developed a regression model as an extension of conventional biochemical models. A regression model analyzes and predicts biochemical reactions based on collected scientific data. This model makes it easier to determine the optimal combination. According to BASF, only a few preliminary lab tests, such as determining the unfolding curve of the enzyme, are necessary. The obtained data are entered into the computer model, which then computes the optimal combination of reaction temperature and solvent concentration for the best possible enzyme performance.
“This sounds simple, but it considerably improves the efficiency of biocatalytic processes and gives us a new understanding of enzymatic catalysis,” Stefan Seemayer, global head of computational protein engineering at BASF. The researchers published their findings in
Nature Communications.
With this new method, different enzymes can be compared more easily with each other, and their performance can be optimized.
“This considerably reduces our efforts to find the most suitable conditions for each new production process. We can therefore conclude our research and development work in the laboratory more quickly and thus begin to scale up production faster. This significantly lowers costs and resource consumption, improving the sustainability of biocatalysis,” Seemayer said.
