The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.
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We thank the European Research Council (ERC Starter Grant, no. 757991 to A.P.G.), the Biotechnology and Biological Sciences Research Council (David Phillips Fellowship BB/M027023/1 to A.P.G.), UK Research and Innovation (Future Leader Fellowship MR/T041722/1 to S.L.L.), the Swiss National Science Foundation (D.H.), ETH Zürich (D.H.) and the Howard Hughes Medical Institute (D.B.) for generous support.
The authors declare no competing interests.
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Lovelock, S.L., Crawshaw, R., Basler, S. et al. The road to fully programmable protein catalysis. Nature 606, 49–58 (2022). https://doi.org/10.1038/s41586-022-04456-z