Journal
NATURE
Volume 606, Issue 7912, Pages 49-58Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41586-022-04456-z
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Funding
- European Research Council (ERC Starter Grant) [757991]
- Biotechnology and Biological Sciences Research Council [BB/M027023/1]
- UK Research and Innovation [MR/T041722/1]
- Swiss National Science Foundation
- ETH Zurich
- Howard Hughes Medical Institute
- European Research Council (ERC) [757991] Funding Source: European Research Council (ERC)
- UKRI [MR/T041722/1] Funding Source: UKRI
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Designing efficient enzymes has a profound impact on chemistry, biotechnology, and medicine. Recent advances in protein engineering and computational methods have made it possible to optimize protein structures and generate efficient enzymes through laboratory evolution. Emerging methods like deep learning hold promise for improving the accuracy of protein design models.
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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