Revealing reaction intermediates in one-carbon elongation by thiamine diphosphate/CoA-dependent enzyme family.

2-Hydroxyacyl-CoA lyase/synthase (HACL/S) is a thiamine diphosphate (ThDP)-dependent versatile enzyme originally discovered in the mammalian α-oxidation pathway. HACL/S natively cleaves 2-hydroxyacyl-CoAs and, in its reverse direction, condenses formyl-CoA with aldehydes or ketones. The one-carbon elongation biochemistry based on HACL/S has enabled the use of molecules derived from greenhouse gases as biomanufacturing feedstocks. We investigated several HACL/S family members with high activity in the condensation of formyl-CoA and aldehydes, and distinct chain-length specificities and kinetic parameters. Our analysis revealed the structures of enzymes in complex with acyl-CoA substrates and products, several covalent intermediates, bound ThDP and ADP, as well as the C-terminal active site region. One of these observed states corresponds to the intermediary α-carbanion with hydroxymethyl-CoA covalently attached to ThDP. This research distinguishes HACL/S from related sub-families and identifies key residues involved in substrate binding and catalysis. These findings expand our knowledge of acyloin-condensation biochemistry and offer attractive prospects for biocatalysis using carbon elongation.

Machine Learning-Supported Enzyme Engineering toward Improved CO2-Fixation of Glycolyl-CoA Carboxylase.

Glycolyl-CoA carboxylase (GCC) is a new-to-nature enzyme that catalyzes the key reaction in the tartronyl-CoA (TaCo) pathway, a synthetic photorespiration bypass that was recently designed to improve photosynthetic CO2 fixation. GCC was created from propionyl-CoA carboxylase (PCC) through five mutations. However, despite reaching activities of naturally evolved biotin-dependent carboxylases, the quintuple substitution variant GCC M5 still lags behind 4-fold in catalytic efficiency compared to its template PCC and suffers from futile ATP hydrolysis during CO2 fixation. To further improve upon GCC M5, we developed a machine learning-supported workflow that reduces screening efforts for identifying improved enzymes. Using this workflow, we present two novel GCC variants with 2-fold increased carboxylation rate and 60% reduced energy demand, respectively, which are able to address kinetic and thermodynamic limitations of the TaCo pathway. Our work highlights the potential of combining machine learning and directed evolution strategies to reduce screening efforts in enzyme engineering.