The road to fully programmable protein catalysis

Sarah L. Lovelock, Rebecca Crawshaw, Sophie Basler, Colin Levy, David Baker (), Donald Hilvert () and Anthony P. Green ()
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Sarah L. Lovelock: School of Chemistry, University of Manchester
Rebecca Crawshaw: School of Chemistry, University of Manchester
Sophie Basler: ETH Zürich
Colin Levy: School of Chemistry, University of Manchester
David Baker: University of Washington
Donald Hilvert: ETH Zürich
Anthony P. Green: School of Chemistry, University of Manchester

Nature, 2022, vol. 606, issue 7912, 49-58

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41586-022-04456-z

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