NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reported that takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7–12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4–6 and 2–4 are hotspots to generate yellow–orange and red POTs, respectively. Based on these findings, a “zipper-bag” model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico.

中文翻译：

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NanoCluster 信标的大规模并行选择

纳米簇信标 (NCB) 是多色银纳米簇探针，其荧光可以通过称为激活剂的近端 DNA 链激活或调节。虽然一对激活剂中的单核苷酸差异可能导致截然不同的激活结果，称为极性相反双胞胎 (POT)，但使用传统的低通量表征方法很难发现新的 POT-NCB。在此，报道了一种高通量选择方法，该方法利用重新设计的下一代测序芯片来筛选约 40 000 个激活剂序列的激活荧光。研究发现，18 个核苷酸长的激活剂的 7-12 位核碱基对于产生明亮的 NCB 至关重要，而 4-6 位和 2-4 位分别是生成黄橙色和红色 POT 的热点。基于这些发现，提出了一个“拉链袋”模型，可以解释这些热点如何促进不同银簇发色团的形成并改变其化学产率。将高通量筛选与机器学习算法相结合，建立了一条在计算机上设计明亮、多色 NCB 的流程。