Anthracycline chemotherapeutics such as doxorubicin and daunorubicin remain indispensable in cancer treatment but possess narrow therapeutic windows, motivating the development of reliable molecular recognition elements for therapeutic drug monitoring, biosensing, and toxicity mitigation. Although an anthracycline aptamer has previously been obtained using immobilized daunorubicin, it lacks a well-defined secondary structure and exhibits poor affinity after truncation. Here, we employed capture-SELEX, which uses free target molecules in solution without immobilization, to isolate DNA aptamers that specifically recognize doxorubicin. The selection yielded three distinct sequence families with well-defined secondary structures and low-nanomolar affinities. Using intrinsic fluorescence quenching, isothermal titration calorimetry, and NMR spectroscopy, we rigorously distinguished specific aptamer recognition from nonspecific intercalation and demonstrated rapid, Mg2+-independent binding. The strongest aptamer, DOX-9, bound doxorubicin with a dissociation constant of 6 nM and also recognized daunorubicin with nanomolar affinity. Compared with the previously reported aptamer, the new sequences exhibit higher affinity, clearer structural features, and greater amenability to truncation. Importantly, they significantly inhibited doxorubicin uptake by HeLa cells, demonstrating potential as functional antidotes in addition to sensing elements. Together, these results establish a new generation of anthracycline aptamers and highlight capture-SELEX as an effective strategy for selecting aptamers against DNA-interacting small molecules.
Meheta Datta, Celine Fidella, Lashaun N. Coote et al.· ACS Chemical Biology· 0 citations
Molecular docking is increasingly used to infer aptamer–target interactions, yet most studies rely on computationally predicted aptamer structures rather than experimentally determined ones. Using a benchmark set of aptamers with known high‐resolution structures, we show that commonly used modeling approaches, including RNAComposer and AlphaFold3, fail to reliably reproduce aptamer conformations, particularly at the binding sites critical for molecular recognition. Key limitations include the use of A‐form RNA models to represent B‐form DNA structures, the prediction of ligand‐free rather than ligand‐bound conformations, and the scarcity of experimentally determined aptamer structures for training machine‐learning models. Using the theophylline aptamer, for which high‐resolution structures are available in both DNA and RNA forms, we systematically evaluated each step of the standard docking workflow. We found that structure‐prediction errors generate incorrect binding pockets, docking scores fail to distinguish theophylline from caffeine despite a 250,000‐fold difference in affinity, and molecular dynamics simulations do not overcome these shortcomings. Together, these results reveal fundamental weaknesses in current aptamer docking workflows and caution against using docking‐derived models to infer binding mechanisms in the absence of experimental structural data.
ThT fluorescence is a useful label-free method to study voriconazole aptamers and that aptamers can be used to probe target binding to nanoscale host molecules and that aptamers can be used to probe target binding to nanoscale host molecules.