Synthetic DNA walkers are artificially designed DNA self-assemblies with the capability of performing quasimechanical movement at the micro/nanoscale and have shown extensive promise in biosensing, intracellular imaging, and drug delivery. However, DNA walkers are usually constructed by covalently or coordinately binding DNA strands specifically to hard surfaces, thereby greatly limiting their movement efficiency. Herein, we report an intraparticle and interparticle transferable DNA walker (dynamic micelle-supported DNA walker, DMwalker) constructed by immobilizing walking tracks and walking arms onto the corona of DNA micelles according to the principle of Watson−Crick base pairing. The DNAzyme-powered walking arm can drive the intraparticle and interparticle movements of the DM-walker due to the fact that the dynamic structure of the DNA micelle helps overcome the spatial barrier between the arms and tracks in the system, resulting in high walking efficiency. Moreover, the whole DM-walker can be constructed by self-assembly, getting rid of the tedious process and low efficiency of fixing DNA strands on hard surfaces. Taking miRNA-10b as a model target, the DM-walker demonstrates high walking efficiency (reaction duration of 20 min) and high sensitivity (LOD of 87 pM). The proposed DM-walker provides an avenue to develop novel DNA walkers on dynamic interfaces and holds great potential in clinical diagnosis.
DNA-templated silver nanoclusters (AgNCs) are widely used as fluorescent probes in various fields owing to their structural stability and fluorescence tunability. The selection of suitable DNA sequences and synthesis conditions with a predictable method is therefore needed, which benefits the preparation of DNA-templated AgNCs with the desired properties and further extension of their application in other fields. Here, we develop and propose a deep learning protocol based on recurrent neural network (RNN) algorithms to predict the fluorescence properties of hairpin-DNA-templated AgNCs (H-AgNCs). Using the stem sequence of hairpin DNA and experimental parameters as input features, the RNN model can identify the number of fluorescence emission peak of H-AgNCs and the fluorescence color of single-peaked H-AgNCs (spH-AgNCs) with the training accuracies of 81.4% and 83.0%, respectively. For each color category, the DNA base distributions of the hairpin stem on the testing set are consistent with those of the training set, indicating the good generalization ability of the constructed model. In addition, a prediction accuracy of 56% is experimentally verified using 20 testing samples, which is 2.3 times higher than that of the random probability, indicating the potential uses of the model for experimental guidance.
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