Grant: Discovery Grants Program - Individual
Université d'Ottawa
$79,000.00
Next-generation computational enzyme design for the creation of efficient artificial biocatalysts
FY 2023-24
Summary
Enzymes are the most efficient catalysts known. They can accelerate chemical reactions by more than a billion times, display unmatched selectivity, and are completely biodegradable. If we could create, from scratch, artificial enzymes that can catalyze any chemical reaction with high efficiency, it would open the door to highly valuable biotechnologies that are currently inaccessible using natural enzymes. These include biocatalytic syntheses of fine chemicals and pharmaceuticals, treatment of metabolic disorders, and bioremediation of contaminated water and soil. In this proposed research program, we will develop next-generation enzyme design methods and use them to create efficient artificial biocatalysts for the synthesis of valuable chemicals. These methods will be based on the multistate protein design algorithms that we developed in the previous funding cycle, which allow proteins to be modeled, not as a single structure as is traditionally done, but as structural ensembles that more realistically represent the range of conformations that these molecules can sample in solution. This is important because enzymes must "move" in order to catalyze chemical reactions with extreme precision and efficiency. To date, all published examples of computational enzyme design have focussed on the design of a single structural state, not taking into account the ability of enzymes to exchange between multiple states, and thereby yielded artificial enzymes with catalytic efficiencies several orders of magnitude worse than those of their natural counterparts. The development and experimental validation of our proposed multistate enzyme design algorithms will be undertaken in the following research themes. 1) We will develop methods to design complete enzymatic catalytic cycles, instead of considering only a single state as was done previously, and use them to create efficient biocatalysts for multistep reactions of industrial interest. 2) We will develop methods to predict mutations far from the active site that will have a favorable effect on catalysis, an essential requirement for the creation of highly active enzymes that nevertheless remains elusive. 3) We will develop multistate design methods to control enzyme conformational equilibria and eventually create large conformational changes required for more complex reactions. In the long term, we will combine these methods to create biocatalysts `on-demand' for valuable biotechnological applications. The proposed research program will provide world-class research training, in both computational and experimental techniques, to a diverse group of trainees, helping them acquire skills highly sought after by the pharmaceutical, biotech, and bioproducts industries. As enzymes can be applied to a wide range of commercially and societally relevant problems, our proposed research will yield new technologies required to tackle important challenges of 21st century Canada in industry, the environment and medicine.
Program
Discovery Grants Program - Individual
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2021-03484
Name nom
Chica, Roberto
Department département
Department of Chemistry and Biomolecular Sciences
Organization id
28
Institution établissement
Université d'Ottawa
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2021
Award amount
79000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1504
Committee name en
Chemistry
Committee name fr
Chimie
Area of application code
1201
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Life sciences (including biotechnology)
Area of application fr
Sciences de la vie (y compris biotechnologie)
Research subject code
3400
Research subject group en
Physical chemistry
Research subject group fr
Physical chemistry
Research subject en
Chimie physique
Research subject fr
Chimie physique
Application title
Next-generation computational enzyme design for the creation of efficient artificial biocatalysts
Keyword
biocatalysis; computational protein design; de novo enzyme design; molecular modelling; molecular simulation; protein engineering; protein solution NMR; protein structure; protein X-ray crystallography
Application summary
Enzymes are the most efficient catalysts known. They can accelerate chemical reactions by more than a billion times, display unmatched selectivity, and are completely biodegradable. If we could create, from scratch, artificial enzymes that can catalyze any chemical reaction with high efficiency, it would open the door to highly valuable biotechnologies that are currently inaccessible using natural enzymes. These include biocatalytic syntheses of fine chemicals and pharmaceuticals, treatment of metabolic disorders, and bioremediation of contaminated water and soil. In this proposed research program, we will develop next-generation enzyme design methods and use them to create efficient artificial biocatalysts for the synthesis of valuable chemicals. These methods will be based on the multistate protein design algorithms that we developed in the previous funding cycle, which allow proteins to be modeled, not as a single structure as is traditionally done, but as structural ensembles that more realistically represent the range of conformations that these molecules can sample in solution. This is important because enzymes must "move" in order to catalyze chemical reactions with extreme precision and efficiency. To date, all published examples of computational enzyme design have focussed on the design of a single structural state, not taking into account the ability of enzymes to exchange between multiple states, and thereby yielded artificial enzymes with catalytic efficiencies several orders of magnitude worse than those of their natural counterparts. The development and experimental validation of our proposed multistate enzyme design algorithms will be undertaken in the following research themes. 1) We will develop methods to design complete enzymatic catalytic cycles, instead of considering only a single state as was done previously, and use them to create efficient biocatalysts for multistep reactions of industrial interest. 2) We will develop methods to predict mutations far from the active site that will have a favorable effect on catalysis, an essential requirement for the creation of highly active enzymes that nevertheless remains elusive. 3) We will develop multistate design methods to control enzyme conformational equilibria and eventually create large conformational changes required for more complex reactions. In the long term, we will combine these methods to create biocatalysts `on-demand' for valuable biotechnological applications. The proposed research program will provide world-class research training, in both computational and experimental techniques, to a diverse group of trainees, helping them acquire skills highly sought after by the pharmaceutical, biotech, and bioproducts industries. As enzymes can be applied to a wide range of commercially and societally relevant problems, our proposed research will yield new technologies required to tackle important challenges of 21st century Canada in industry, the environment and medicine.