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Completed
Novel enzyme activities by combining computational design and laboratory evolution
FunderNetherlands Organisation for Scientific Research (NWO)
Duration01 Jan 2006 - 05 Mar 2008
Project code700.54.416
Open Access mandatePublications: NoResearch data: No
Identifiers
Project timeline100%
Description
Laboratory evolution is a powerful technique for the optimization of enzymes. With the iterative introduction of a limited number of mutations, variant enzymes with optimized features are screened and selected. This random mutagenesis approach has been proven to be successful in numerous cases. However, introduction of novel substrate specificities in a biocatalyst that require the simultaneous substitution of a relatively large number of amino acid residues, is less feasible due to practical limitations. The recent development of generalized dead-end elimination (DEE) algorithms for computational design of proteins, for the first time enables the efficient in silico reshaping of binding sites, with the goal to adjust them to recognize new ligands. To date, however, the DEE algorithms have not been applied to catalytically active enzymes. Aim - The aim of this project is to engineer an enzyme?s substrate specificity by (i) applying DEE algorithms for the design of substrate binding sites, (ii) producing the predicted variant enzymes, and (iii) optimizing the enzyme activities by laboratory evolution. Approach - The Sulfolobus solfataricus KDG aldolase will serve as a model enzyme: it can easily be produced in E. coli, it is very robust, and it has relatively broad substrate specificity. Using the available 3D structure of S. solfataricus KDG aldolase, its active site will be designed to accommodate alternative substrate donors and acceptors, for which it has no activity. A basic level of activity should be reached by computational design of the active site. The calculated modified active sites will be constructed and characterized. Enzyme activity will be optimized further by laboratory evolution. Elements of innovation - The application of DEE algorithms for the computational design of the active sites of enzymes, and its combination with laboratory evolution for optimization of enzymes is unprecedented. This powerful approach is anticipated to be widely applicable in protein engineering.
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Laboratory evolution is a powerful technique for the optimization of enzymes. With the iterative introduction of a limited number of mutations, variant enzymes with optimized features are screened and selected. This random mutagenesis approach has been proven to be successful in numerous cases. However, introduction of novel substrate specificities in a biocatalyst that require the simultaneous substitution of a relatively large number of amino acid residues, is less feasible due to practical limitations. The recent development of generalized dead-end elimination (DEE) algorithms for computational design of proteins, for the first time enables the efficient in silico reshaping of binding sites, with the goal to adjust them to recognize new ligands. To date, however, the DEE algorithms have not been applied to catalytically active enzymes. Aim - The aim of this project is to engineer an enzyme?s substrate specificity by (i) applying DEE algorithms for the design of substrate binding sites, (ii) producing the predicted variant enzymes, and (iii) optimizing the enzyme activities by laboratory evolution. Approach - The Sulfolobus solfataricus KDG aldolase will serve as a model enzyme: it can easily be produced in E. coli, it is very robust, and it has relatively broad substrate specificity. Using the available 3D structure of S. solfataricus KDG aldolase, its active site will be designed to accommodate alternative substrate donors and acceptors, for which it has no activity. A basic level of activity should be reached by computational design of the active site. The calculated modified active sites will be constructed and characterized. Enzyme activity will be optimized further by laboratory evolution. Elements of innovation - The application of DEE algorithms for the computational design of the active sites of enzymes, and its combination with laboratory evolution for optimization of enzymes is unprecedented. This powerful approach is anticipated to be widely applicable in protein engineering.
