Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the SN2 Step

Abstract

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 S_N2 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 S_N2 step. On the simulation side, we provided molecular insights into these designs for the S_N2 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 S_N2 step than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized through natural evolution.

Figure 1.

DhlA-catalyzed reaction and the structure of DhlA. (A) Reaction Scheme. S_N2 displacement of the chlorine atom from DCE, involving covalent bonding of DCE to the carboxylate group of D124, followed by hydrolysis, producing 2-chloroethanol. (B) Crystal structure of DhlA bound to DCE (PDB ID: 2DHC). (C) Close-up of the substrate (carbon atoms are in blue, chlorine atoms are in green) and selected active site residues, with F128 and L262 (underlined) highlighted as the mutated positions in the designs.

Figure 2.

Predicting the catalytic activity of DhlA using the generative MaxEnt model. (A) Schematic representation of the MaxEnt model. The model captures both site conservation and pairwise site couplings in the MSA constructed with DhlA homologues. (B) The MaxEnt model predicts both the overall catalytic activity (k_cat) and the S_N2 step (k₂). Lower $E(S)$ (or higher $P(S)$) indicates higher activity. The k_cat, k₂ and statistical energies $E(S)$ are presented in Table S1.

Figure 3.

Normalized first derivative curves from differential scanning fluorimetry for DhlA protein variants. The peak maxima correspond to the melting temperatures (T_m), measured in 100 mM glycine buffer at pH 8.6.

Figure 4.

Model used for fitting transient kinetic data of DhlA protein variants.

Figure 5.

EVB simulations of DhlA protein variants. (A) Comparison of computed (pink) and experimental (blue) activation energies (ΔG^‡) for the S_N2 reaction. Experimental ΔG^‡ values are derived from the Eyring equation based on k₂. (B−E) Representative molecular structures of native DhlA in its reactant state (B) and transition state (C), and of the L262V variant in its reactant state (D) and transition state (E). Only DCE and the side chains of key amino acids (D124, W125, F128, W175, V262) are shown. Atom color code: H in gray, C in slate blue, N in deep blue, O in red, Cl in green.

Figure 6.

Metadynamics simulations of DhlA protein variants. Angle θ(O−C−Cl) as a function of distance d(O−C) in (A) wild type, (B) L262V, (C) F128L/L262V, and (D) F128L/L262I.