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Int. J. Mol. Sci. 2012, 13(10), 12428-12460; doi:10.3390/ijms131012428
Review

Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution

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Received: 20 August 2012; in revised form: 16 September 2012 / Accepted: 17 September 2012 / Published: 28 September 2012
(This article belongs to the Special Issue Enzyme Optimization and Immobilization)
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Abstract: Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.
Keywords: de novo enzyme design; enzyme redesign; protein engineering; directed evolution; computational enzymology de novo enzyme design; enzyme redesign; protein engineering; directed evolution; computational enzymology
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Barrozo, A.; Borstnar, R.; Marloie, G.; Kamerlin, S.C.L. Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution. Int. J. Mol. Sci. 2012, 13, 12428-12460.

AMA Style

Barrozo A, Borstnar R, Marloie G, Kamerlin SCL. Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution. International Journal of Molecular Sciences. 2012; 13(10):12428-12460.

Chicago/Turabian Style

Barrozo, Alexandre; Borstnar, Rok; Marloie, Gaël; Kamerlin, Shina Caroline Lynn. 2012. "Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution." Int. J. Mol. Sci. 13, no. 10: 12428-12460.


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