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Published March 27, 2001 | Published
Journal Article Open

Computational method to reduce the search space for directed protein evolution


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.

Additional Information

© 2001 by The National Academy of Sciences Communicated by William A. Goddard III, California Institute of Technology, Pasadena, CA, December 22, 2000 (received for review May 26, 2000) C.A.V. is supported by a National Science Foundation graduate research fellowship and by a California Institute of Technology Initiative in Computational Molecular Biology, a Burroughs Wellcome-funded program for science at the interface. Financial support was provided by the Howard Hughes Medical Institute (S.L.M.). We thank Hue Sun Chan, Peter Kollman, Alan Fersht, John Yin, and Walter Fontana for advance readings of this manuscript and critical comments. The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

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