Fluorescence Color by Data-Driven Design of Genomic Silver Clusters
Fluorescence Color by Data-Driven Design of Genomic Silver Clusters
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DOI：
10.1021/acsnano.8b03404
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发表时间：
2018-08-01
期刊：
影响因子：
17.1
通讯作者：
Gwinn, Elisabeth G.
中科院分区：
文献类型：
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作者：
Copp, Stacy M.;Gorovits, Alexander;Gwinn, Elisabeth G.
DNA核碱基序列控制DNA稳定的银簇的大小，导致它们众所周知但知之甚少的序列调谐颜色。用于模板化簇的可能DNA序列的巨大空间挑战了对序列如何选择簇属性的理解，并且限制了采用这些簇的应用程序的设计。我们调查的基因组作用的DNA序列的荧光银簇使用数据驱动的方法。采用快速平行银簇合成和荧光分析法，我们确定了银簇产物稳定的1432个不同的DNA寡聚体的荧光光谱。通过将模式识别算法应用于这个大的实验数据集，我们发现了某些DNA碱基模式或“图案”，它们与具有相似荧光光谱的银簇相关。这些基序被用于机器学习分类器中，以预测性地设计用于特定荧光色带的DNA模板序列。我们的方法提高了330%的银团簇的峰值发射波长超过660 nm的模板的选择性。发现的碱基基序也提供了DNA序列如何控制银簇大小和颜色的物理见解。这种DNA稳定的银团簇颜色的预测设计方法显示出机器学习和数据挖掘的潜力，以提高纳米材料设计的精度和效率，即使是软物质-无机混合系统，其特征在于一个非常大的参数空间。
DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. We investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or "motifs," that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. This predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.