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The road to fully programmable protein catalysis

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Bibliographic Details
Title: The road to fully programmable protein catalysis
Authors: Sarah L. Lovelock, Rebecca Crawshaw, Sophie Basler, Colin Levy, David Baker, Donald Hilvert, Anthony P. Green
Source: Lovelock, S L, Crawshaw, R, Basler, S, Levy, C, Baker, D, Hilvert, D & Green, A P 2022, ' The road to fully programmable protein catalysis ', Nature, vol. 606, no. 7912, pp. 49-58 . https://doi.org/10.1038/s41586-022-04456-z
Nature
Publisher Information: Springer Science and Business Media LLC, 2022.
Publication Year: 2022
Subject Terms: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Biotechnology/methods, Biocatalysis, Proteins, Protein Engineering, Protein Engineering/methods, Proteins/chemistry, Biotechnology
Description: 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.
Document Type: Article
Language: English
ISSN: 1476-4687
0028-0836
DOI: 10.1038/s41586-022-04456-z
Access URL: https://pubmed.ncbi.nlm.nih.gov/35650353
https://research.manchester.ac.uk/en/publications/92554aca-4e60-473d-84f3-c0bd076dd2bf
https://doi.org/10.1038/s41586-022-04456-z
Rights: Springer TDM
Accession Number: edsair.doi.dedup.....0e5cf58193b8a92b2d6d2f271265fc43
Database: OpenAIRE
Description
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.
ISSN:14764687
00280836
DOI:10.1038/s41586-022-04456-z