High-throughput activator sequence selection for silver nanocluster beacons

Kuo, Yu An; Jung, Cheulhee; Chen, Yu An; Rybarski, James R.; Nguyễn, Tùng Lâm; Chen, Yin An; Kuo, Hung Che; Zhao, Oliver S.; Madrid, Victor A.; Chen, Yuan I.; Liu, Yen-Liang; Hawkins, John A.; Petty, Jeffrey T.; Finkelstein, Ilya J.; Yeh, Hsin Chih

doi:10.1117/12.2510649

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…The Yeh group has recently pioneered high-throughput screening of Ag N -DNA sensors, which may significantly expedite progress in this area. 238–240 …”

Section: Future Directions and Challengesmentioning

confidence: 99%

Structure and luminescence of DNA-templated silver clusters

et al. 2021

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“…The Yeh group has recently pioneered high-throughput screening of Ag N -DNA sensors, which may significantly expedite progress in this area. 238–240 …”

Section: Future Directions and Challengesmentioning

confidence: 99%

Structure and luminescence of DNA-templated silver clusters

et al. 2021

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“…DNA’s combinatorial nature makes machine learning (ML) approaches well-suited for probing Ag N -DNA “sequence–structure–property” relationships. Because first-principles models for Ag N -DNAs are nascent, experimental data are necessary to enable ML. ,− We previously developed high-throughput chemical synthesis and optical characterization to generate data libraries that connect DNA sequences to visible and NIR fluorescence colors of Ag N -DNAs. , Because Ag N -DNAs naturally fall into color classes based on magic number properties, we employed supervised ML to determine how sequence encodes Ag N -DNA color class. (Supervised ML involves the use of labeled data sets of inputs, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Because first-principles models for Ag N -DNAs are nascent, 33 experimental data are necessary to enable ML. 30 , 34 − 36 We previously developed high-throughput chemical synthesis and optical characterization 30 to generate data libraries that connect DNA sequences to visible and NIR fluorescence colors of Ag N -DNAs. 24 , 35 Because Ag N -DNAs naturally fall into color classes based on magic number properties, 30 we employed supervised ML to determine how sequence encodes Ag N -DNA color class.…”

Section: Introductionmentioning

confidence: 99%

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

et al. 2022

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DNA can stabilize silver nanoclusters (Ag N -DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA’s vast sequence space challenges the design and discovery of Ag N -DNAs with tailored properties. In particular, Ag N -DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive Ag N -DNAs. By combining high-throughput experimentation and machine learning with fundamental information from Ag N -DNA crystal structures, we distill the salient DNA sequence features that determine Ag N -DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of Ag N -DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive Ag N -DNAs by 12.3 times as compared to training data, nearly doubling the number of known Ag N -DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure–property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data.

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“…Copp, et al, developed the highthroughput experimental approach described in Section 4, 43 and the Yeh group has developed a platform for rapid screening of ''light-up'' Ag N -DNA probes called NanoCluster Beacons (NCBs) 80 based on Illumina MiSeq chips. 81 An alternate approach is to mine the existing literature for reports of Ag N -DNAs and their stabilizing DNA sequences. New, et al compiled a detailed list of 133 different DNA template sequences and the properties of their associated Ag N -DNAs.…”

mentioning

confidence: 99%