Computational design of serine hydrolases

Abstract

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion¹ to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k _cat /K _m ) up to 3.8 x 10³ M^-1 s^-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

Figure 1.. Design methods.

(A) Active site specific backbone generation with RFdiffusion. Given the geometry of a possible active site configuration, RFdiffusion denoising trajectories generate backbone coordinates which scaffold the site. (B) Generation of active site ensembles with ChemNet. The coordinates of the sidechains around the active site and any bound small molecule for the step in the reaction being considered are randomized, and n samples are carried out to generate an ensemble of predictions. (C) Mechanism of ester hydrolysis by serine hydrolases. (D) Chemnet ensembles for distinct states along the reaction coordinate for hydrolysis of 4MU-Ac for a native serine hydrolase (top, PDB: 1IVY) and a designed serine hydrolase (bottom, josie).

Figure 2.. Functional characterization of designed serine hydrolases.

(A) Chemical schematic of a serine hydrolase active site. (B) Summary of design method and experimental success rate for probe labeling, single turnover acylation, and catalytic turnover for each design round. (C) Chemical schematic depicting probe labeling, acylation, and catalytic turnover. (D) Fold (left) and active site (right) of serine hydrolase design models. (E) Reaction progress curves for the parent design and catalytic residue knockouts. Dashed line represents the enzyme concentration. (F) Michaelis-Menten plots derived from initial (rd1, rd2) or steady state velocities (rd3).

Figure 3.. Structural characterization of designed serine hydrolases.

(A,D) Structural superposition of design models (gray) and crystal structures (rainbow) for super (A) and win (D). (B, E) Active site overlays of design models (gray) and crystal structures (rainbow) of super (B) and win (E) with 2Fo-Fc map shown at 1σ (blue mesh). (C, F) Superposition of substrate binding sites of the design models (gray) and crystal structures (rainbow) with 2Fo-Fc map shown at 1 σ (blue mesh). Distances shown are in Å.

Figure 4.. Computational redesign and more complex folds improve catalysis.

(A) Computational pipeline for redesign of rd3_win. (B,C,D) k_cat (B), K_m (C), and k_ca/K_m (D) of parent rd3_win compared to computational redesigns. (E,F) Structural superposition of win1 (E) and win31 (F) design and crystal structure. (G,H,I,J) Design models (G,I) and Michaelis-Menten plots (H,J) for active designs with distinct folds and active site structures. (K) Chemical and structural comparison of n and n+1 oxyanion hole motifs. (L) Design model of active design that utilizes two backbone amide oxyanion hole contacts, one from an n+1 backbone amide. (M) Michaelis-Menten plot of active design momi.

Figure 5.. ChemNet ensembles reveal geometric determinants of catalysis.

(A) Frequencies of catalytic Ser-His H-bond formation in ChemNet ensembles of each reaction intermediate, grouped by experimental outcome. (B) Apo ChemNet ensembles of representative inactive (top) and acylating (bottom) designs. (C) Median angle (α) between serine Oγ, histidine Nϵ and Cϵ across Chemnet ensembles of inactive and acylating designs. (D) Apo ChemNet ensembles of representative inactive (top) and acylating (bottom) designs, angle indicates median α. (Ε) ΑΕΙ ChemNet ensemble H-bond frequencies for designs that undergo acylation or full turnover. (F) ChemNet ensembles of the apo state for an acylating (top) and multiple turnover design (bottom). (G) ChemNet ensembles of the AEI state for a representative design that undergoes acylation (top) and a design that catalyzes turnover (bottom). Measurements shown represent median distances (Å) of key H-bonds indicated for each ensemble and percentages represent frequency of H-bond formation across all ChemNet trajectories. (H) Newman projections of serine g+ and g- rotameric states (left). (I) Chemnet ensembles of an acylating design (top) and a design that catalyzes turnover (bottom). (J) Median serine X₁, angle across TI1 and AEI state Chemnet ensembles for designs that catalyze acylation or turnover (left). Median serine X₁, angle across TI1 and AEI state Chemnet ensembles for the same designs grouped by number of oxyanion hole hydrogen bonds. (K) AEI state Chemnet ensembles for win, win1, and win31, with percent of frames with correct oxyanion hole rotamer, Ser X₁, angle, and catalytic Ser-His H-hbond distance shown.