A structured methodology for NADES selection and formulation for enzymatic reactions.

Natural deep eutectic solvents (NADES) show great promise as media for enzymatic reactions in sectors where (bio)compatibility with natural or medical products is a must. Whereas in theory they can be tailored to the envisioned reaction, ensuring optimized yields, to date the knowledge is predominantly empirical, with some mechanistic reports giving a fragmented view at best. Therefore, even merely explaining experimental observations is not straightforward, let alone making predictions. This doctoral study aims at building a structured, holistic understanding of the effect of NADES media on enzymatic reactions, whereby effects on solubility, solvation, viscosity, inhibition and denaturation will be distinguished. The solubility, solvation energy and viscosity will be predicted by first principles and molecular dynamics calculations, serving as input for a group contribution model using machine learning. Experiments will train and validate the model, and learnings from observed reaction kinetics will be further benchmarked against molecular dynamics calculations of enzyme structures and interactions in NADES. Structural changes of the enzyme will be demonstrated using Raman optical activity spectroscopy. The combination of these methods ensures fundamental knowledge acquisition, while the group contribution model is part of a structured methodology. The findings of this project are transferable to other uses of NADES.

Keywords:BIOCATALYSIS, CHEMICAL MODELING, SOLVENTS, CHEMICAL ANALYSIS

Disciplines:Modelling and simulation, Biocatalysis, Chemical product design and formulation not elsewhere classified