Success probability of high-affinity DNA aptamer generation by genetic alphabet expansion
Michiko KimotoiD, Hui Pen TaniD, Yaw Sing TaniD, Nur Afiqah Binte Mohd Mislan, Ichiro HiraoiD
Abstract
SummaryNucleic 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 to 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.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 15
- FWCI
- 1.33
- Field percentile
- top 22%
- Assay
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- AptaDiff: de novo design and optimization of aptamers based on diffusion models
Zhen Wang, Ziqi Liu, … Xiaolin Li·Briefings in BioinformaticsPeer-reviewed·Sep 23, 2024
- De novo design of protein-binding aptamers through deep reinforcement learning assembly of nucleic acid fragments
Gaoxing Guo, Liangwei Guo, … Qiang Huang·bioRxiv·Jun 1, 2025
- A direct selection strategy for isolating aptamers with pH-sensitive binding activity
Chelsea K. L. Gordon, Michael S. Eisenstein, Hyongsok Tom Soh·ACS SensorsPeer-reviewed·Dec 6, 2018
- AptaBLE: A Deep Learning Platform for Aptamer Generation and Analysis
Sawan Patel, Keith Fraser, … Sherwood Yao·bioRxiv·Feb 20, 2026
- Flow-cell based technology for massively parallelcharacterization of base-modified DNA aptamers
- A high-throughput strategy for enhancing aptamer performance across different environmental conditions
L. T. Wan, A. M. Yoshikawa, … H. T. Soh·bioRxiv·Sep 13, 2022