Fox RJ, Davis SC, Mundorff EC, Newman LM, Gavrilovic V, Ma SK, Chung LM, Ching C, Tam S, Muley S, Grate J, Gruber J, Whitman JC, Sheldon RA, Huisman GW. Improving catalytic function by ProSAR-driven enzyme evolution.
Nat Biotechnol 2007;
25:338-44. [PMID:
17322872 DOI:
10.1038/nbt1286]
[Citation(s) in RCA: 317] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2006] [Accepted: 01/17/2007] [Indexed: 01/25/2023]
Abstract
We describe a directed evolution approach that should find broad application in generating enzymes that meet predefined process-design criteria. It augments recombination-based directed evolution by incorporating a strategy for statistical analysis of protein sequence activity relationships (ProSAR). This combination facilitates mutation-oriented enzyme optimization by permitting the capture of additional information contained in the sequence-activity data. The method thus enables identification of beneficial mutations even in variants with reduced function. We use this hybrid approach to evolve a bacterial halohydrin dehalogenase that improves the volumetric productivity of a cyanation process approximately 4,000-fold. This improvement was required to meet the practical design criteria for a commercially relevant biocatalytic process involved in the synthesis of a cholesterol-lowering drug, atorvastatin (Lipitor), and was obtained by variants that had at least 35 mutations.
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