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1: Proc Natl Acad Sci U S A. 2001 Mar 27;98(7):3778-83.
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Computational method to reduce the search space for directed protein evolution.

Voigt CA, Mayo SL, Arnold FH, Wang ZG.

Biochemistry Option, Divisions of Biology and Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.

We introduce a computational method to optimize the in vitro evolution of proteins. Simulating evolution with a simple model that statistically describes the fitness landscape, we find that beneficial mutations tend to occur at amino acid positions that are tolerant to substitutions, in the limit of small libraries and low mutation rates. We transform this observation into a design strategy by applying mean-field theory to a structure-based computational model to calculate each residue's structural tolerance. Thermostabilizing and activity-increasing mutations accumulated during the experimental directed evolution of subtilisin E and T4 lysozyme are strongly directed to sites identified by using this computational approach. This method can be used to predict positions where mutations are likely to lead to improvement of specific protein properties.

Publication Types:
PMID: 11274394 [PubMed - indexed for MEDLINE]

PMCID: PMC31129