Photophysical properties of fluorescent imaging biological probes of nucleic acids: SAC-CI and TD-DFT Study.


Journal

Journal of computational chemistry
ISSN: 1096-987X
Titre abrégé: J Comput Chem
Pays: United States
ID NLM: 9878362

Informations de publication

Date de publication:
05 01 2019
Historique:
received: 31 05 2018
accepted: 13 07 2018
pubmed: 26 8 2018
medline: 2 6 2020
entrez: 26 8 2018
Statut: ppublish

Résumé

Recently, exciton-controlled hybridization-sensitive fluorescent oligonucleotide (ECHO) probe, which shows strong emission in the near-infrared region via hybridization to the target DNA and/or RNA strand, has been developed. In this work, photophysical properties of the chromophores of these probes and the fluorescent mechanism have been investigated by the SAC-CI and TD-DFT calculations. Three fluorescent cyanine chromophores whose excitation is challenging for TD-DFT methods, have been examined regarding the photo-absorption and emission spectra. The SAC-CI method well reproduces the experimental values with respect to transition energies, while the quantitative prediction by TD-DFT calculations is difficult for these chromophores. Some stable structures of H-aggregate system were computationally located and two of the configurations were examined for the photo-absorption. The present results support for the assumption based on experimental measurement in which strong fluorescence is due to the monomer unit in nearly planar structure and its suppression of probes is to the H-aggregates of two exciton units. Stokes shifts of these three chromophores were qualitatively reproduced by the theoretical calculations, while the energy splitting due to H-aggregate in the hybridized probe was slightly overestimated. © 2018 Wiley Periodicals, Inc.

Identifiants

pubmed: 30144120
doi: 10.1002/jcc.25553
doi:

Substances chimiques

Fluorescent Dyes 0
Nucleic Acids 0
Oligonucleotide Probes 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

127-134

Informations de copyright

© 2018 Wiley Periodicals, Inc.

Auteurs

Takafumi Shiraogawa (T)

SOKENDAI, The Graduate University for Advanced Studies, Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan.

G Candel (G)

Institut de Recherche de Chimie Paris, PSL Research University, CNRS, Chimie ParisTech, 11 rue Pierre et Marie Curie, Paris, F-75005, France.

Ryoichi Fukuda (R)

Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Kyoto, 615-8245, Japan.
Department of Molecular Engineering, Graduate School of Engineering, Kyoto University, Nishikyo-ku, Kyoto, 615-8520, Japan.

Ilaria Ciofini (I)

Institut de Recherche de Chimie Paris, PSL Research University, CNRS, Chimie ParisTech, 11 rue Pierre et Marie Curie, Paris, F-75005, France.

Carlo Adamo (C)

Institut de Recherche de Chimie Paris, PSL Research University, CNRS, Chimie ParisTech, 11 rue Pierre et Marie Curie, Paris, F-75005, France.
Institut Universitaire de France, 103 Boulevard Saint Michel, F-75005, Paris, France.

Akimitsu Okamoto (A)

Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan.
Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.

Masahiro Ehara (M)

SOKENDAI, The Graduate University for Advanced Studies, Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan.
Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Kyoto, 615-8245, Japan.
Institute for Molecular Science and Research Center for Computational Science, Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
1.00
Humans Magnetic Resonance Imaging Brain Infant, Newborn Infant, Premature
Humans Algorithms Software Artificial Intelligence Computer Simulation

Unsupervised learning for real-time and continuous gait phase detection.

Dollaporn Anopas, Yodchanan Wongsawat, Jetsada Arnin
1.00
Humans Gait Neural Networks, Computer Unsupervised Machine Learning Walking

Classifications MeSH