4.7 Article

Ligase-Mediated Threose Nucleic Acid Synthesis on DNA Templates

Journal

ACS SYNTHETIC BIOLOGY
Volume 8, Issue 2, Pages 282-286

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.8b00511

Keywords

threose nucleic acid; xeno nucleic acid; ligase; enzymatic ligation

Funding

  1. National Science Foundation [1607111]
  2. DARPA Folded Non-Natural Polymers with Biological Function Fold F(x) Program [N66001-16-2-4061]
  3. Direct For Biological Sciences [1607111] Funding Source: National Science Foundation
  4. Div Of Molecular and Cellular Bioscience [1607111] Funding Source: National Science Foundation

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Ligases are a class of enzymes that catalyze the formation of phosphodiester bonds between an oligonucleotide donor with a 5' terminal phosphate and an oligonucleotide acceptor with a 3' terminal hydroxyl group. Here, we wished to explore the substrate specificity of naturally occurring DNA and RNA ligases to determine whether the molecular recognition of these enzymes is sufficiently general to synthesize alternative genetic polymers with backbone structures that are distinct from those found in nature. We chose threose nucleic acid (TNA) as a model system, as TNA is known to be biologically stable and capable of undergoing Darwinian evolution. Enzyme screening and reaction optimization identified several ligases that can recognize TNA as either the donor or acceptor strand with DNA. Less discrimination occurs on the acceptor strand indicating that the determinants of substrate specificity depend primarily on the composition of the donor strand. Remarkably, T3 and T7 ligases were able to join TNA homopolymers together, which is surprising given that the TNA backbone is one atom shorter than that of DNA. In this reaction, the base composition of the ligation junction strongly favors the formation of A-T and A-G linkages. We suggest that these results will enable the assembly of TNA oligonucleotides of lengths beyond what is currently possible by solid-phase synthesis and provide a starting point for further optimization by directed evolution.

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