The road to fully programmable protein catalysis

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 cofact...

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Published in:Nature (London) Vol. 606; no. 7912; pp. 49 - 58
Main Authors: Lovelock, Sarah L., Crawshaw, Rebecca, Basler, Sophie, Levy, Colin, Baker, David, Hilvert, Donald, Green, Anthony P.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 02.06.2022
Nature Publishing Group
Subjects:
119/118
631/92/606
639/638/77/603
Binding sites
Biocatalysis
Biocatalysts
Biotechnology
Biotechnology - methods
Biotechnology - trends
Catalysis
Catalysts
Cofactors
Computer applications
Deep learning
Design
Engineering
Enzymes
Humanities and Social Sciences
Laboratories
Ligands
multidisciplinary
Protein engineering
Protein Engineering - methods
Protein Engineering - trends
Protein structure
Proteins
Proteins - chemistry
Proteins - metabolism
Review Article
Robust design
Science
Science (multidisciplinary)
Structural analysis
Zinc
ISSN:0028-0836, 1476-4687, 1476-4687
Online Access:Get full text
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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. Recent progress in computational enzyme design, active site engineering and directed evolution are reviewed, highlighting methodological innovations needed to deliver improved designer biocatalysts.
AbstractList 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. Recent progress in computational enzyme design, active site engineering and directed evolution are reviewed, highlighting methodological innovations needed to deliver improved designer biocatalysts.
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.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.
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.
Author Lovelock, Sarah L.
Baker, David
Basler, Sophie
Green, Anthony P.
Crawshaw, Rebecca
Hilvert, Donald
Levy, Colin
Author_xml – sequence: 1
  givenname: Sarah L.
  orcidid: 0000-0002-4584-3189
  surname: Lovelock
  fullname: Lovelock, Sarah L.
  organization: Manchester Institute of Biotechnology, School of Chemistry, University of Manchester
– sequence: 2
  givenname: Rebecca
  surname: Crawshaw
  fullname: Crawshaw, Rebecca
  organization: Manchester Institute of Biotechnology, School of Chemistry, University of Manchester
– sequence: 3
  givenname: Sophie
  orcidid: 0000-0002-4594-2670
  surname: Basler
  fullname: Basler, Sophie
  organization: Laboratory of Organic Chemistry, ETH Zürich
– sequence: 4
  givenname: Colin
  surname: Levy
  fullname: Levy, Colin
  organization: Manchester Institute of Biotechnology, School of Chemistry, University of Manchester
– sequence: 5
  givenname: David
  orcidid: 0000-0001-7896-6217
  surname: Baker
  fullname: Baker, David
  email: dabaker@uw.edu
  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington, Howard Hughes Medical Institute, University of Washington
– sequence: 6
  givenname: Donald
  orcidid: 0000-0002-3941-621X
  surname: Hilvert
  fullname: Hilvert, Donald
  email: donald.hilvert@org.chem.ethz.ch
  organization: Laboratory of Organic Chemistry, ETH Zürich
– sequence: 7
  givenname: Anthony P.
  orcidid: 0000-0003-0454-1798
  surname: Green
  fullname: Green, Anthony P.
  email: anthony.green@manchester.ac.uk
  organization: Manchester Institute of Biotechnology, School of Chemistry, University of Manchester
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35650353$$D View this record in MEDLINE/PubMed
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SubjectTerms 119/118
631/92/606
639/638/77/603
Binding sites
Biocatalysis
Biocatalysts
Biotechnology
Biotechnology - methods
Biotechnology - trends
Catalysis
Catalysts
Cofactors
Computer applications
Deep learning
Design
Engineering
Enzymes
Humanities and Social Sciences
Laboratories
Ligands
multidisciplinary
Protein engineering
Protein Engineering - methods
Protein Engineering - trends
Protein structure
Proteins
Proteins - chemistry
Proteins - metabolism
Review Article
Robust design
Science
Science (multidisciplinary)
Structural analysis
Zinc
Title The road to fully programmable protein catalysis
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