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Abstract The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochromecto achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY’s potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution. 

Due to the scarcity of C–F bond-forming enzymatic reactions in nature and the contrasting prevalence of organofluorine moieties in bioactive compounds, developing biocatalytic fluorination reactions represents a pre-eminent challenge in enzymology, biocatalysis and synthetic biology. Additionally, catalytic enantioselective C(sp3)–H fluorination remains a challenging problem facing synthetic chemists. Although many non-haem iron halogenases have been discovered to promote C(sp3)–H halogenation reactions, efforts to convert these iron halogenases to fluorinases have remained unsuccessful. Here we report the development of an enantioselective C(sp3)–H fluorination reaction, catalysed by a plant-derived non-haem enzyme 1-aminocyclopropane-1-carboxylic acid oxidase (ACCO), which is repurposed for radical rebound fluorination. Directed evolution afforded a C(sp3)–H fluorinating enzyme ACCOCHF displaying 200-fold higher activity, substantially improved chemoselectivity and excellent enantioselectivity, converting a range of substrates into enantioenriched organofluorine products. Notably, almost all the beneficial mutations were found to be distal to the iron centre, underscoring the importance of substrate tunnel engineering in non-haem iron biocatalysis. Computational studies reveal that the radical rebound step with the Fe(III)–F intermediate has a low activation barrier of 3.4 kcal mol−1 and is kinetically facile. 

Developing synthetically useful enzymatic reactions that are not known in biochemistry and organic chemistry is an important challenge in biocatalysis. Through the synergistic merger of photoredox catalysis and pyridoxal 5′-phosphate (PLP) biocatalysis, we developed a pyridoxal radical biocatalysis approach to prepare valuable noncanonical amino acids, including those bearing a stereochemical dyad or triad, without the need for protecting groups. Using engineered PLP enzymes, either enantiomeric product could be produced in a biocatalyst-controlled fashion. Synergistic photoredox-pyridoxal radical biocatalysis represents a powerful platform with which to discover previously unknown catalytic reactions and to tame radical intermediates for asymmetric catalysis. 

Herein we disclosed an unprecedented photochemically driven nickel‐catalyzed carboxylative Buchwald–Hartwig amination to access a wide range of aryl carbamate derivatives. This reaction is performed under mild condition of temperature and atmospheric pressure of CO2 starting from commercially available (hetero)aryl iodides/bromides derivatives and alkyl amines preventing the formation of hazardous and/or toxic waste. Moreover, preliminary mechanistic investigations including stochiometric experiments as well as DFT calculations allow us to shed light on the reaction mechanism.