Challenges and Advances in Validating Enzyme Design Proposals: The Case of Kemp Eliminase Catalysis

Department of Chemistry, 418 SGM Building, University of Southern California, 3620 McClintock Avenue, Los Angeles, California 90089-1062, USA.
Biochemistry (Impact Factor: 3.02). 03/2011; 50(18):3849-58. DOI: 10.1021/bi200063a
Source: PubMed


One of the fundamental challenges in biotechnology and biochemistry is the ability to design effective enzymes. Despite recent progress, most of the advances on this front have been made by placing the reacting fragments in the proper places, rather than by optimizing the preorganization of the environment, which is the key factor in enzyme catalysis. Thus, rational improvement of the preorganization would require approaches capable of evaluating reliably the actual catalytic effect. This work considers the catalytic effects in different Kemp eliminases as a benchmark for a computer-aided enzyme design. It is shown that the empirical valence bond provides a powerful screening tool, with significant advantages over current alternative strategies. The insights provided by the empirical valence bond calculations are discussed with an emphasis on the ability to analyze the difference between the linear free energy relationships obtained in solution and those found in the enzymes. We also point out the trade-off between the reliability and speed of the calculations and try to determine what it takes to realize reliable computer-aided screening.

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Available from: Maria P Frushicheva, Jul 02, 2014
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    • "To address this issue, a number of screening approaches were presented [36,37] that could be used to screen for potential mutation hotspots (which could then be tested by EVB), as well as to predict stability change upon mutation, however, we will leave the discussion of these to Section 4, which deals with currently available approaches for performing in silico directed evolution. Here, we would just like to comment on a recent work [79] that used iterative MD and experimental analysis to explore previously inactive artificial KEs and used insights from MD and structural analysis to improve activity. "
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