Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the SN2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model. We then designed lower-order protein variants of haloalkane dehalogenase using the model. Kinetic measurements confirmed the successful design of protein variants that enhance catalytic activity, above that of the wild type, in the overall reaction and in particular in the SN2 step. On the simulation side, we provided molecular insights into these designs for the SN2 step using the empirical valence bond (EVB) and metadynamics simulations. The EVB calculations showed activation barriers consistent with experimental reaction rates, while examining the effect of amino acid replacements on the electrostatic effect on the activation barrier and the consequence of water penetration, as well as the extent of ground state destabilization/stabilization. Metadynamics simulations emphasize the importance of the substrate positioning in enzyme catalysis. Overall, our AI-guided approach successfully enabled the design of a variant with a faster rate for the SN2 step than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized through natural evolution.

• MeSH
• Hydrolases * chemistry   metabolism   genetics   MeSH
• Kinetics MeSH
• Protein Engineering MeSH
• Molecular Dynamics Simulation MeSH
• Artificial Intelligence * MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• haloalkane dehalogenase MeSH Browser
• Hydrolases * MeSH

Haloalkane dehalogenases are enzymes that catalyze the cleavage of carbon-halogen bonds in halogenated compounds. They serve as model enzymes for studying structure-function relationships of >100.000 members of the α/β-hydrolase superfamily. Detailed kinetic analysis of their reaction is crucial for understanding the reaction mechanism and developing novel concepts in protein engineering. Fluorescent substrates, which change their fluorescence properties during a catalytic cycle, may serve as attractive molecular probes for studying the mechanism of enzyme catalysis. In this work, we present the development of the first fluorescent substrates for this enzyme family based on coumarin and BODIPY chromophores. Steady-state and pre-steady-state kinetics with two of the most active haloalkane dehalogenases, DmmA and LinB, revealed that both fluorescent substrates provided specificity constant two orders of magnitude higher (0.14-12.6 μM-1 s-1) than previously reported representative substrates for the haloalkane dehalogenase family (0.00005-0.014 μM-1 s-1). Stopped-flow fluorescence/FRET analysis enabled for the first time monitoring of all individual reaction steps within a single experiment: (i) substrate binding, (ii-iii) two subsequent chemical steps and (iv) product release. The newly introduced fluorescent molecules are potent probes for fast steady-state kinetic profiling. In combination with rapid mixing techniques, they provide highly valuable information about individual kinetic steps and mechanism of haloalkane dehalogenases. Additionally, these molecules offer high specificity and efficiency for protein labeling and can serve as probes for studying protein hydration and dynamics as well as potential markers for cell imaging.

• Keywords
• Enzyme kinetics, Fluorescent substrate, Haloalkane dehalogenase, Mechanism, Protein labeling,
• Publication type
• Journal Article MeSH