Cofactor Specificity and Extreme Robustness in the Catalytic Landscape of Ketol-Acid Reductoisomerase: An Evolutionary Reconstruction

Navigating trade-offs between efficiency, specificity, and stability is a central challenge in biocatalyst design. Ancestral sequence reconstruction (ASR) is an effective approach for such design as it leverages the natural trajectories of evolution to infer sequences with different properties under a variety of selective pressures. Here, ASR was applied to systematically map the catalytic landscape of the NAD(P)H-dependent ketol-acid reductoisomerase (KARI) superfamily, which catalyze the rate-limiting step ((S)-2-acetolactate to (R)-2,3-dihydroxy-3-isovalerate (DHIV)), in the conversion of glucose, a renewable feedstock, to the platform chemical, isobutanol. Reconstructed ancestral variants exhibit a variety of properties not seen in extant KARIs, including enhanced catalytic efficiencies (up to 25-fold), longevity (total turnover numbers >106), and melting temperatures approaching 100 °C (40 °C above benchmarks). Notably, we expose latent high-performance states within this fitness landscape; one ancestral variant (N79) displays solvent-mediated catalytic activation, exemplifying an enzyme that combines resource-efficient catalysis, process-level robustness, and compatibility with high-yield production of isobutanol.