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Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. (Kamerlin Lab)
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. (Kamerlin Lab)
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. (Kamerlin Lab)
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. (Kamerlin)
2012 (English)In: International Journal of Molecular Sciences, ISSN 1422-0067, E-ISSN 1422-0067, Vol. 13, no 10, 12428-12460 p.Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2012. Vol. 13, no 10, 12428-12460 p.
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-185064DOI: 10.3390/ijms131012428ISI: 000310677800019OAI: oai:DiVA.org:uu-185064DiVA: diva2:570570
Funder
EU, European Research Council, 306474Swedish Research Council, 2010-5026
Available from: 2012-11-20 Created: 2012-11-19 Last updated: 2017-12-07Bibliographically approved

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Publisher's full texthttp://www.mdpi.com/1422-0067/13/10/12428

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