Kimoto, M., Tan, H. P., Tan, Y. S., Mislan, N. A. B. M., & Hirao, I. (2023). Success probability of high-affinity DNA aptamer generation by genetic alphabet expansion. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1871). https://doi.org/10.1098/rstb.2022.0031
Abstract:
Nucleic acid aptamers as antibody alternatives bind specifically to target molecules. These aptamers are generated by isolating candidates from libraries with random sequence fragments, through an evolutionary engineering system. We recently reported a high-affinity DNA aptamer generation method that introduces unnatural bases (UBs) as a fifth letter into the library, by genetic alphabet expansion. By incorporating hydrophobic UBs, the affinities of DNA aptamers to target proteins are increased over 100-fold, as compared with those of conventional aptamers with only the natural four letters. However, there is still plenty of room for improvement of the methods for routinely generating high-affinity UB-containing DNA (UB-DNA) aptamers. The success probabilities of the high-affinity aptamer generation depend on the existence of the aptamer candidate sequences in the initial library. We estimated the success probabilities by analysing several UB-DNA aptamers that we generated, as examples. In addition, we investigated the possible improvement of conventional aptamer affinities by introducing one UB at specific positions. Our data revealed that UB-DNA aptamers adopt specific tertiary structures, in which many bases including UBs interact with target proteins for high affinity, suggesting the importance of the UB-DNA library design.
This article is part of the theme issue ‘Reactivity and mechanism in chemical and synthetic biology’.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Institute of Bioengineering and Bioimaging and the Bioinformatics Institute (Biomedical Research Council, Agency for Science, Technology and Research, Singapore) - 2021 Horizontal Technology Programme Office Seed Fund
Grant Reference no. : C-21-13-18-002