Phosphorylation transfer reactions, central to both energy metabolism and nucleic-acid synthesis, are ubiquitous in biological systems. Mechanism pathways range from dissociative to concerted and associative forms, with transition state geometries influenced by the leaving group’s ability and the nucleophile’s reactivity. While the mechanism has been clarified, a comprehensive predictive model for reaction kinetics has not yet been established, as the phosphorylation transfer reaction relies on numerous parameters. This project aims to develop a general predictive framework for phosphorylation transfer rate constants by combining high-throughput experimentation, curated kinetic datasets, quantum-chemical calculations, and statistical modelling. We expanded an automatic kinetic high-throughput workflow to gather kinetic constants and benchmarked computational methods to assess their ability to reproduce experimental trends. Finally, we combined experimental and computational data to construct a predictive model using linear free-energy relationships and multivariate regression techniques, which enable the accurate estimation of rate constants across diverse phosphorylation transfer systems.
Natalie Schürmann