Screening for Novel Fluorescent Nucleobase Analogues Using Computational and Experimental Methods: 2-Amino-6-chloro-8-vinylpurine (2a6cl8vp) As a Case Study.
The Journal of Physical Chemistry B(2023)
Temple Univ
Abstract
Novel fluorescent nucleic acid base analogues (FBAs) with improved optical properties are needed in a variety of biological applications. 2-Amino-6-chloro-8-vinylpurine (2A6Cl8VP) is structural analogue of two existing highly fluorescent FBAs, 2-aminopurine (2AP) and 8-vinyladenine (8VA), and can therefore be expected to have similar base pairing as well as better optical properties compared to its counterparts. In order to determine the absorption and fluorescence properties of 2A6Cl8VP, as a first step, we used TD-DFT calculations and the polarizable continuum model for simulating the solvents and computationally predicted absorption and fluorescence maxima. To test the computational predictions, we also synthesized 2A6Cl8VP and measured its UV/vis absorbance, fluorescence emission, and fluorescence lifetime. The computationally predicted absorbance and fluorescence maxima of 2A6Cl8VP are in reasonable agreement to the experimental values and are significantly redshifted compared to 2AP and 8VA, allowing for its specific excitation. The fluorescence quantum yield of 2A6Cl8VP, however, is significantly lower than those of 2AP and 8VA. Overall, 2A6Cl8VP is a novel fluorescent nucleobase analogue, which can be useful in studying structural, biophysical, and biochemical applications.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined