Machine learning-assisted directed evolution of plant Rubisco
Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) is foundational tolife on Earth, catalyzing carbon dioxide (CO2) fixation to generate biomass. However,Rubisco is a slow and inefficient enzyme that has proven challenging to engineer. Weapplied the structure-informed machine learning (ML) model ESM-IF1 to identifyplausible amino acid sites and changes in the tobacco Rubisco large subunit (NtRbcL)to target for directed evolution. ML-guided library design followed by selection inRubisco-dependent Escherichia coli isolated multiple enriched variants withimprovements to catalytic efficiency. Several improved variants carried amino acidchanges not found in the evolutionary lineage of plants, demonstrating that ML-guidedprotein design can explore functional sequence space beyond what can be observedfrom natural evolutionary sequence diversity. Most prominently, the NtRbcLT391I variantimproved the carboxylation rate by 29% and aerobic carboxylation efficiency by 43%.Our findings demonstrate the utility of ML-assisted evolution towards potentiallyenhancing crop productivity through the discovery of unique, higher carboxylationcapacity Rubisco.