Data-driven development of an enzyme cascade for the synthesis of cyclic dinucleotides

• Applicant: Professorin Dr.-Ing. Katrin Rosenthal
• Subject Area: Biological Process Engineering
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 535167833

Several impressive biocatalytic multi-step reactions, so-called in vitro enzyme cascades, have been developed for the synthesis of complex molecules with pharmaceutical relevance. However, the development and optimization of enzyme cascades is challenging because the performance may be affected by destabilizing or inhibitory interactions between cascade components. In the proposed project, an iterative data-driven approach combining modelling and simulation with wet-lab experiments will be developed to optimize a cascade for the synthesis of pharmaceutically relevant molecules in terms of production performance. As a model cascade, the synthesis of 2′3′-cyclic GMP-AMP (2′3′-cGAMP) is selected, which is a second messenger molecule that shows promise as adjuvant for vaccines and cancer therapies. A cascade with seven reaction steps will be developed from nucleosides that are phosphorylated by nucleoside kinases (NKs) and polyphosphate kinases (PPKs) and finally cyclized by a cyclic GMP-AMP synthase (cGAS). The research objectives that will be focused on in the project are i) investigating the substrate scope of the enzymes (NKs, PPKs, and cGAS) that will then allow the selection of appropriate catalysts for the target cascade, ii) designing a cascade with complex cross-reactivities for the synthesis of cyclic dinucleotides from nucleosides, and iii) applying machine learning algorithms to solve the multi-parameter problem, i.e. the performance enhancement of enzyme-catalyzed reaction cascades as a function of the initial reaction conditions. The project will be an initiation for further interdisciplinary scientific and engineering projects. The developed method can be transferred to other reaction cascades or other multi-parameter optimization tasks besides enzyme cascades. Ultimately, this will lead to an accelerated development of new valuable and active compounds.

• DFG Programme: Research Grants