Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

Area of Science:

• Nanomaterials Science
• Biophysics
• Computational Chemistry

Background:

• DNA-templated silver nanoclusters exhibit sequence-dependent fluorescence colors.
• Understanding the precise relationship between DNA sequence and cluster properties is challenging due to the vast sequence space.
• This limits the rational design of silver nanoclusters for specific applications.

Purpose of the Study:

• To investigate the role of DNA sequence in determining the fluorescence spectra of silver nanoclusters.
• To develop a data-driven method for predicting and designing DNA sequences for specific silver nanocluster colors.
• To gain physical insights into the sequence-structure-property relationships in these hybrid nanomaterials.

Main Methods:

• Synthesized silver nanoclusters using 1432 distinct DNA oligomers in a high-throughput manner.
• Characterized the fluorescence spectra of the resulting silver nanoclusters using fluorimetry.
• Applied pattern recognition algorithms and machine learning classifiers to identify DNA sequence motifs correlated with specific fluorescence properties.

Main Results:

• Identified specific DNA base patterns (motifs) that correlate with distinct fluorescence spectra of silver nanoclusters.
• Developed machine learning models capable of predicting DNA sequences for desired fluorescence colors.
• Achieved a 330% improvement in template selectivity for silver nanoclusters emitting beyond 660 nm.

Conclusions:

• DNA sequence motifs are key determinants of silver nanocluster size and color.
• A data-driven, machine learning approach enables predictive design of DNA templates for targeted nanomaterial properties.
• This strategy enhances precision and efficiency in designing DNA-stabilized silver nanoclusters for advanced applications.