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Computational method to reduce the search space for directed protein evolution
 

Summary: Computational method to reduce the search space for
directed protein evolution
Christopher A. Voigt*, Stephen L. Mayo
, Frances H. Arnold§
, and Zhen-Gang Wang§
*Biochemistry Option, Divisions of Biology and Chemistry and Chemical Engineering, Howard Hughes Medical Institute and Division of Biology, and
§Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
Communicated by William A. Goddard III, California Institute of Technology, Pasadena, CA, December 22, 2000 (received for review May 26, 2000)
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 com-
putational 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

  

Source: Arnold, Frances H. - Division of Chemistry and Chemical Engineering, California Institute of Technology
Voigt, Christopher A. - Department of Pharmaceutical Chemistry, University of California at San Francisco

 

Collections: Biology and Medicine; Biotechnology; Chemistry