メンターシップによる発見(Discovery through mentorship)

2023-07-24 カリフォルニア大学校アーバイン校(UCI)

<関連情報>

• https://news.uci.edu/2023/07/24/discovery-through-mentorship/
• https://pubs.acs.org/doi/full/10.1021/jacs.3c01366
• https://pubs.acs.org/doi/10.1021/acsnano.2c05390

DNA安定化銀ナノクラスター上の塩化物リガンド Chloride Ligands on DNA-Stabilized Silver Nanoclusters

Anna Gonzàlez-Rosell, Sami Malola, Rweetuparna Guha, Nery R. Arevalos, María Francisca Matus, Meghen E. Goulet, Esa Haapaniemi, Benjamin B. Katz, Tom Vosch, Jiro Kondo, Hannu Häkkinen, and Stacy M. Copp
Journal of the American Chemical Society  Published:May 8, 2023
DOI:https://doi.org/10.1021/jacs.3c01366

Abstract

DNA-stabilized silver nanoclusters (Ag_N-DNAs) are known to have one or two DNA oligomer ligands per nanocluster. Here, we present the first evidence that Ag_N-DNA species can possess additional chloride ligands that lead to increased stability in biologically relevant concentrations of chloride. Mass spectrometry of five chromatographically isolated near-infrared (NIR)-emissive Ag_N-DNA species with previously reported X-ray crystal structures determines their molecular formulas to be (DNA)₂[Ag₁₆Cl₂]⁸⁺. Chloride ligands can be exchanged for bromides, which red-shift the optical spectra of these emitters. Density functional theory (DFT) calculations of the 6-electron nanocluster show that the two newly identified chloride ligands were previously assigned as low-occupancy silvers by X-ray crystallography. DFT also confirms the stability of chloride in the crystallographic structure, yields qualitative agreement between computed and measured UV–vis absorption spectra, and provides interpretation of the ³⁵Cl-nuclear magnetic resonance spectrum of (DNA)₂[Ag₁₆Cl₂]⁸⁺. A reanalysis of the X-ray crystal structure confirms that the two previously assigned low-occupancy silvers are, in fact, chlorides, yielding (DNA)₂[Ag₁₆Cl₂]⁸⁺. Using the unusual stability of (DNA)₂[Ag₁₆Cl₂]⁸⁺ in biologically relevant saline solutions as a possible indicator of other chloride-containing Ag_N-DNAs, we identified an additional Ag_N-DNA with a chloride ligand by high-throughput screening. Inclusion of chlorides on Ag_N-DNAs presents a promising new route to expand the diversity of Ag_N-DNA structure–property relationships and to imbue these emitters with favorable stability for biophotonics applications.

化学に基づく機械学習により、近赤外蛍光を発するDNA安定化銀ナノクラスターの発見が可能になる Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

Peter Mastracco, Anna Gonzàlez-Rosell, Joshua Evans, Petko Bogdanov, and Stacy M. Copp
ACS Nano  Published:September 20, 2022
DOI:https://doi.org/10.1021/acsnano.2c05390

Abstract

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.