Design and Evolution of Enantioselective Photoredox Enzymes
Lead Research Organisation:
University of Manchester
Department Name: Engineering and Physical Sciences
Abstract
Project Summary
Enzymes are exceptionally powerful catalysts that recognize molecular substrates and process them in active sites. They are generally built from just 20 amino acids, and their catalytic machinery is typically assembled from chemical groups in the amino-acid side chains. But fewer than half of these side chains contain functional groups that can participate in enzyme catalytic cycles, which severely restricts the range of mechanisms conceivable within enzyme active sites. This raises the intriguing question of whether the catalytic repertoire of enzymes could be expanded by using an extended 'alphabet' of amino acids that offers a wider range of side chains for catalysis. In recent years our group have taken major steps towards achieving this ambition (e.g. Nature 2022, 611, 709. Nature 2019, 570, 219).
Our approach exploits engineered cellular translation components to selectively install non-canonical amino acids containing functional side chains. Genetically encoding the non-canonical functionality offers enormous advantages over alternative methods for chemically modifying protein structure: it greatly facilitates the production of well-defined, homogeneous proteins; it allows the non-canonical amino acid to be introduced at any site, in any protein scaffold; and, perhaps most significantly, it allows for rapid optimization of enzyme properties using directed evolution. Inspired by mechanistic strategies from small molecule organocatalysis and photocatalysis, we have recently employed a combination of genetic code expansion, computational enzyme design and laboratory evolution to create enzymes that exploit non-canonical amino acids as key catalytic elements. These studies open up new and exciting opportunities to enzyme designers and engineers which will be fully explored within this PhD studentship. Free from the constraints of the genetic code, the student will employ our advanced enzyme engineering techniques to create enzymes with functions not observed in Nature, that were previously thought inaccessible to the field of biocatalysis.
The project will specifically aim to create enzymes that contain organic photoredox motifs embedded within designed protein active sites. Upon irradiation with visible light, these artificial cofactors become potent electron donors or acceptors to trigger a wide range of valuable photoredox processes. Here we can take advantage of molecular recognition elements provided by the protein scaffold to achieve enantioselective conversions, to enhance catalytic efficiencies and to tune the spectroscopic and photophysical properties of cofactor. Significantly, promising starting designs can be substantially improved through iterative rounds of directed evolution to afford highly efficient and selective de novo photoenzymes for producing high value molecules.
The project takes a truly innovative approach to merge the fields of biocatalysis and photocatalysis, and thus is perfectly aligned to the strategic priorities of the iCAT network.
Enzymes are exceptionally powerful catalysts that recognize molecular substrates and process them in active sites. They are generally built from just 20 amino acids, and their catalytic machinery is typically assembled from chemical groups in the amino-acid side chains. But fewer than half of these side chains contain functional groups that can participate in enzyme catalytic cycles, which severely restricts the range of mechanisms conceivable within enzyme active sites. This raises the intriguing question of whether the catalytic repertoire of enzymes could be expanded by using an extended 'alphabet' of amino acids that offers a wider range of side chains for catalysis. In recent years our group have taken major steps towards achieving this ambition (e.g. Nature 2022, 611, 709. Nature 2019, 570, 219).
Our approach exploits engineered cellular translation components to selectively install non-canonical amino acids containing functional side chains. Genetically encoding the non-canonical functionality offers enormous advantages over alternative methods for chemically modifying protein structure: it greatly facilitates the production of well-defined, homogeneous proteins; it allows the non-canonical amino acid to be introduced at any site, in any protein scaffold; and, perhaps most significantly, it allows for rapid optimization of enzyme properties using directed evolution. Inspired by mechanistic strategies from small molecule organocatalysis and photocatalysis, we have recently employed a combination of genetic code expansion, computational enzyme design and laboratory evolution to create enzymes that exploit non-canonical amino acids as key catalytic elements. These studies open up new and exciting opportunities to enzyme designers and engineers which will be fully explored within this PhD studentship. Free from the constraints of the genetic code, the student will employ our advanced enzyme engineering techniques to create enzymes with functions not observed in Nature, that were previously thought inaccessible to the field of biocatalysis.
The project will specifically aim to create enzymes that contain organic photoredox motifs embedded within designed protein active sites. Upon irradiation with visible light, these artificial cofactors become potent electron donors or acceptors to trigger a wide range of valuable photoredox processes. Here we can take advantage of molecular recognition elements provided by the protein scaffold to achieve enantioselective conversions, to enhance catalytic efficiencies and to tune the spectroscopic and photophysical properties of cofactor. Significantly, promising starting designs can be substantially improved through iterative rounds of directed evolution to afford highly efficient and selective de novo photoenzymes for producing high value molecules.
The project takes a truly innovative approach to merge the fields of biocatalysis and photocatalysis, and thus is perfectly aligned to the strategic priorities of the iCAT network.
Organisations
People |
ORCID iD |
Anthony Green (Primary Supervisor) | |
Celine Zurr (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S023755/1 | 31/03/2019 | 29/09/2027 | |||
2886507 | Studentship | EP/S023755/1 | 30/09/2023 | 29/09/2027 | Celine Zurr |