Efficient Selection of Biomineralizing DNA Aptamers Using Deep Sequencing and Population Clustering

DNA-based information systems drive the combinatorial optimization processes of natural evolution, including the evolution of biominerals. Advances in high-throughput DNA sequencing expand the power of DNA as a potential information platform for combinatorial engineering, but many applications remain to be developed due in part to the challenge of handling large amounts of sequence data. Here we employ high-throughput sequencing and a recently developed clustering method (AutoSOME) to identify single-stranded DNA sequence families that bind specifically to ZnO semiconductor mineral surfaces. These sequences were enriched from a diverse DNA library after a single round of screening, whereas previous screening approaches typically require 5–15 rounds of enrichment for effective sequence identification. The consensus sequence of the largest cluster was poly d(T)<sub>30</sub>. This consensus sequence exhibited clear aptamer behavior and was shown to promote the synthesis of crystalline ZnO from aqueous solution at near-neutral pH. This activity is significant, as the crystalline form of this wide-bandgap semiconductor is not typically amenable to solution synthesis in this pH range. High-resolution TEM revealed that this DNA synthesis route yields ZnO nanoparticles with an amorphous–crystalline core–shell structure, suggesting that the mechanism of mineralization involves nanoscale coacervation around the DNA template. We thus demonstrate that our new method, termed <u>S</u>ingle round <u>E</u>nrichment of <u>L</u>igands by deep <u>Seq</u>uencing (SEL-Seq), can facilitate biomimetic synthesis of technological nanomaterials by accelerating combinatorial selection of biomolecular–mineral interactions. Moreover, by enabling direct characterization of sequence family demographics, we anticipate that SEL-Seq will enhance aptamer discovery in applications employing additional rounds of screening.