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Selection Platforms for Directed Evolution in Synthetic Biology

Overview
Specialty Biochemistry
Date 2016 Aug 17
PMID 27528765
Citations 28
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Abstract

Life on Earth is incredibly diverse. Yet, underneath that diversity, there are a number of constants and highly conserved processes: all life is based on DNA and RNA; the genetic code is universal; biology is limited to a small subset of potential chemistries. A vast amount of knowledge has been accrued through describing and characterizing enzymes, biological processes and organisms. Nevertheless, much remains to be understood about the natural world. One of the goals in Synthetic Biology is to recapitulate biological complexity from simple systems made from biological molecules-gaining a deeper understanding of life in the process. Directed evolution is a powerful tool in Synthetic Biology, able to bypass gaps in knowledge and capable of engineering even the most highly conserved biological processes. It encompasses a range of methodologies to create variation in a population and to select individual variants with the desired function-be it a ligand, enzyme, pathway or even whole organisms. Here, we present some of the basic frameworks that underpin all evolution platforms and review some of the recent contributions from directed evolution to synthetic biology, in particular methods that have been used to engineer the Central Dogma and the genetic code.

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References
1.
Doi N, Yanagawa H . STABLE: protein-DNA fusion system for screening of combinatorial protein libraries in vitro. FEBS Lett. 1999; 457(2):227-30. DOI: 10.1016/s0014-5793(99)01041-8. View

2.
Liu R, Barrick J, Szostak J, Roberts R . Optimized synthesis of RNA-protein fusions for in vitro protein selection. Methods Enzymol. 2000; 318:268-93. DOI: 10.1016/s0076-6879(00)18058-9. View

3.
Irving R, Coia G, Roberts A, Nuttall S, Hudson P . Ribosome display and affinity maturation: from antibodies to single V-domains and steps towards cancer therapeutics. J Immunol Methods. 2001; 248(1-2):31-45. DOI: 10.1016/s0022-1759(00)00341-0. View

4.
Ghadessy F, Ong J, Holliger P . Directed evolution of polymerase function by compartmentalized self-replication. Proc Natl Acad Sci U S A. 2001; 98(8):4552-7. PMC: 31872. DOI: 10.1073/pnas.071052198. View

5.
Keefe A, Szostak J . Functional proteins from a random-sequence library. Nature. 2001; 410(6829):715-8. PMC: 4476321. DOI: 10.1038/35070613. View