The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination-a model reaction for proton transfer from carbon-with measured rate enhancements of up to 10 5 and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a .200-fold increase in k cat /K m (k cat /K m of 2,600 M 21 s 21 and k cat /k uncat of .10 6 ). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.Naturally occurring enzymes are extraordinarily efficient catalysts 1 . They bind their substrates in a well-defined active site with precisely aligned catalytic residues to form highly active and selective catalysts for a wide range of chemical reactions under mild conditions. Nevertheless, many important synthetic reactions lack a naturally occurring enzymatic counterpart. Hence, the design of stable enzymes with new catalytic activities is of great practical interest, with potential applications in biotechnology, biomedicine and industrial processes. Furthermore, the computational design of new enzymes provides a stringent test of our understanding of how naturally occurring enzymes work. In the past several years, there has been exciting progress in designing new biocatalysts 2,3 .Here we describe the use of our recently developed computational enzyme design methodology 4 to create new enzyme catalysts for a reaction for which no naturally occurring enzyme exists: the Kemp elimination 5,6 . The reaction, shown in Fig. 1a, has been extensively studied as an activated model system for understanding the catalysis of proton abstraction from carbon-a process that is normally restricted by high activation-energy barriers 7,8 . Computational design methodThe first step in our protocol for designing new enzymes is to choose a catalytic mechanism and then to use quantum mechanical transition state calculations to create an idealized active site with protein functional groups positioned so as to maximize transition state stabilization (Fig. 1b). The key step for the Kemp elimination is deprotonation of a carbon by a general base. We chose two different catalytic bases for this purpose: first, the carboxyl group of an aspartate or glutamate side chain, and, second, the imidazole of a histidine positioned and polarized by the carboxyl group of an aspartate or glutamate (we refer to this combination as a His-Asp dyad). The two choices have complementary strengths and weaknesses. The advantage of the carboxylate...

The creation of novel enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Here we describe two new algorithms for enzyme design that employ hashing techniques to allow searching through large numbers of protein scaffolds for optimal catalytic site placement. We also describe an in silico benchmark, based on the recapitulation of the active sites of native enzymes, that allows rapid evaluation and testing of enzyme design methodologies. In the benchmark test, which consists of designing sites for each of 10 different chemical reactions in backbone scaffolds derived from 10 enzymes catalyzing the reactions, the new methods succeed in identifying the native site in the native scaffold and ranking it within the top five designs for six of the 10 reactions. The new methods can be directly applied to the design of new enzymes, and the benchmark provides a powerful in silico test for guiding improvements in computational enzyme design.Keywords: enzyme design; protein design; active site recapitulation; protein-ligand interactions; geometric hashing Enzymes are among the most efficient, specific, and selective catalysts known. The ability to design efficient enzymes for a broad class of different reactions would be of tremendous practical interest for both science and the industry. Furthermore, the rational design of enzymes is a stringent test of our understanding of biological catalysis.There has been exciting progress in enzyme design. On the experimental side, catalytic antibodies, elicited by immunization with transition state analogs, have been shown to possess catalytic activity (Lerner et al. 1991;Hilvert 2000). More recently, several successful enzyme designs have been reported. Kaplan and DeGrado (2004) The computational methods used in enzyme site design to date, such as ORBIT from the Mayo group (Dahiyat and Mayo 1996) and Dezymer from the Hellinga group (Hellinga and Richards 1991), have primarily been used to search for catalytic site placement in one or a small number of scaffolds. In contrast, computational methods for searching for functional sites that employ geometric hashing (Russell 1998) In general, how to evaluate and optimize computational design methods for the creation of new molecules is a nontrivial problem. For robust conclusions, it is desirable to compare alternative methods and parameter choices by comparing results on a representative set of test systems. In the ''protein design cycle'' approach described by Dahiyat and Mayo (1997), alternative choices in a design method are tested by producing designs and experimentally characterizing them, and the choice is selected that produces designs with the desired properties. While this is a very powerful approach, experimentally characterizing a large number of designs for a number of different methods is slow and expensive, and therefore, it is desirable to have a faster and cheaper test. A purely in silico test for monomeric protein design developed in our group based on...

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Although de novo computational enzyme design has been shown to be feasible, the field is still in its infancy: the kinetic parameters of designed enzymes are still orders of magnitude lower than those of naturally occurring ones. Nonetheless, designed enzymes can be improved by directed evolution, as recently exemplified for the designed Kemp eliminase KE07. Random mutagenesis and screening resulted in variants with >200-fold higher catalytic efficiency, and provided insights about features missing in the designed enzyme. Here we describe the optimization of KE70, another designed Kemp eliminase. Amino acid substitutions predicted to improve catalysis in design calculations involving extensive backbone sampling were individually tested. Those proven beneficial were combinatorially incorporated into the originally designed KE70 along with random mutations, and the resulting libraries were screened for improved eliminase activity. Nine rounds of mutation and selection resulted in >400-fold improvement in the catalytic efficiency of the original KE70 design, reflected in both higher kcat and lower KM values, with the best variants exhibiting kcat/KM values of >5x104 s−1M−1. The optimized KE70 variants were characterized structurally and biochemically providing insights into the origins of the improvements in catalysis. Three primary contributions were identified: first, the reshaping of the active site cavity to achieve tighter substrate binding; second, the fine-tuning of the electrostatics around the catalytic His-Asp dyad; and third, stabilization of the active-site dyad in a conformation optimal for catalysis.