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      Rational Design of a Structural and Functional Nitric Oxide Reductase

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          Summary

          Protein design provides an ultimate test of our knowledge about proteins and allows the creation of novel enzymes for biotechnological applications. While progress has been made in designing proteins that mimic native proteins structurally 13 , it is more difficult to design functional proteins 48 . In comparison to recent successes in designing non-metalloproteins 4, 6, 7, 9, 10 , it is even more challenging to rationally design metalloproteins that reproduce both the structure and function of native metalloenzymes 5, 8, 1120 , since protein metal binding sites are much more varied than non-metal containing sites, in terms of different metal ion oxidation states, preferred geometry and metal ion ligand donor sets. Because of their variability, it has been difficult to predict metal binding site properties in silico, as many of the parameters for metal binding sites, such as force fields are ill-defined. Therefore, the successful design of a structural and functional metalloprotein will greatly advance the field of protein design and our understanding of enzymes. Here, we report a successful, rational design of a structural and functional model of a metalloprotein, nitric oxide reductase (NOR), by introducing three histidines and one glutamate, predicted as ligands in the active site of NOR, into the distal pocket of myoglobin. A crystal structure of the designed protein confirms that the minimized computer model contains a heme/non-heme Fe B center that is remarkably similar to that in the crystal structure. This designed protein also exhibits NOR activity. This is the first designed protein that models both the structure and function of NOR, offering insight that the active site glutamate is required for both iron binding and activity. These results show that structural and functional metalloproteins can be rationally designed in silico.

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          Kemp elimination catalysts by computational enzyme design.

          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(-1)s(-1) 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.
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            De novo computational design of retro-aldol enzymes.

            The creation of enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Using new algorithms that rely on hashing techniques to construct active sites for multistep reactions, we designed retro-aldolases that use four different catalytic motifs to catalyze the breaking of a carbon-carbon bond in a nonnatural substrate. Of the 72 designs that were experimentally characterized, 32, spanning a range of protein folds, had detectable retro-aldolase activity. Designs that used an explicit water molecule to mediate proton shuffling were significantly more successful, with rate accelerations of up to four orders of magnitude and multiple turnovers, than those involving charged side-chain networks. The atomic accuracy of the design process was confirmed by the x-ray crystal structure of active designs embedded in two protein scaffolds, both of which were nearly superimposable on the design model.
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              Design of functional metalloproteins.

              Metalloproteins catalyse some of the most complex and important processes in nature, such as photosynthesis and water oxidation. An ultimate test of our knowledge of how metalloproteins work is to design new metalloproteins. Doing so not only can reveal hidden structural features that may be missing from studies of native metalloproteins and their variants, but also can result in new metalloenzymes for biotechnological and pharmaceutical applications. Although it is much more challenging to design metalloproteins than non-metalloproteins, much progress has been made in this area, particularly in functional design, owing to recent advances in areas such as computational and structural biology.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                24 December 2014
                25 November 2009
                24 December 2009
                16 January 2015
                : 462
                : 7276
                : 1079-1082
                Affiliations
                [1 ]Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                [2 ]George L. Clark X-Ray Facility & 3M Materials Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                [3 ]Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                [4 ]Department of Biology, Brookhaven National Laboratory, Upton, NY 11973, USA
                Author notes
                [* ]Correspondence and requests for materials should be addressed to: Y.L. ( yi-lu@ 123456illinois.edu )
                Article
                NIHMS156263
                10.1038/nature08620
                4297211
                19940850
                af34fbb0-2b78-46e7-8103-1d61d6435841

                Reprints and permissions information is available at www.nature.com/reprints

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