B9N9纳米细胞对多巴胺和酪胺神经递质感知的理论研究。

IF 2.5 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ascharya Kumar Kar, Swetapadma Praharaj, Tarun Yadav, Somenath Garai, Dileep Kumar Gupta, Dibyaranjan Rout
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引用次数: 0

摘要

目的:本研究首次证实利用B9N9纳米环作为感知多巴胺和酪胺神经递质的活性物质。方法:利用gaussian sian09软件中的密度泛函理论(DFT)计算B9N9环、多巴胺和酪胺神经递质的优化和振动频率。采用标准泛函(B3LYP)和基集6-311G(d, p)进行计算。自然键轨道(NBO)分析量化了电荷转移,而分子静电势(MEP)作图、电子局域函数(ELF)、还原密度梯度(RDG)分析和分子原子量子理论(QTAIM)表征了分子间相互作用。结果:利用密度泛函理论(B3LYP/6-311G(d, p)), B9N9环对所考虑的神经递质的响应性完全由优化的电子结构、能量、电子和其他性质表征。多巴胺和酪胺的相互作用通过在最有利的构型中形成N-B键来控制。dopamine@B9N9和tyramine@B9N9配合物最稳定构型的相互作用能分别为-22.11和-21.49 kcal/mol,表明神经递质与纳米环存在显著的相互作用,说明B9N9环在这些神经递质传感应用中的应用。计算了HOMO、LUMO、费米能、功函数和能隙等关键常规电子参数,并研究了水介质对这些参数的影响。利用极低频和RGD技术量化了电子密度分布,并探讨了所涉及的相互作用。进行了TD-DFT计算来模拟紫外-可见光谱。此外,进行了分子中原子的量子理论(QTAIM)分析,结果表明dopamine@B9N9和tyramine@B9N9表现出更强的氢键相互作用,导致键强度增加。结论:B9N9纳米化与两种神经递质均有明显的相互作用,其电子特性的改变有助于两种神经递质的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Theoretical investigation on sensing of dopamine and tyramine neurotransmitters by the B9N9 nanoring

Purpose

The investigation concerns the first demonstration of employing the B9N9 nanoring as an active material for sensing dopamine and tyramine neurotransmitters.

Method

The calculations for optimization and vibrational frequencies of B9N9 ring, dopamine, and tyramine neurotransmitters have been executed by using density functional theory (DFT) available in the Gaussian09 suite. The standard functional (B3LYP) coupled with the basis set 6-311G(d, p) have been adopted for calculations. Natural bond orbital (NBO) analysis quantified charge transfer, while molecular electrostatic potential (MEP) mapping, electron localization function (ELF), reduced density gradient (RDG) analysis, and quantum theory of atoms in molecules (QTAIM) characterized intermolecular interactions.

Results

The responsiveness of B9N9 ring towards the considered neurotransmitters is characterized thoroughly by the optimized electronic structures, energies, electronic, and other properties using density functional theory (B3LYP/6-311G(d, p)). The interaction of dopamine and tyramine governs through formation of the N-B dative bond in the most favorable configurations. The interaction energies for the most stable configurations of dopamine@B9N9 and tyramine@B9N9 complexes are found to be −22.11 and −21.49 kcal/mol, respectively, which show a significant interaction of neurotransmitters to nanoring and elucidate the adoption of B9N9 ring in sensing applications for these neurotransmitters. The critical conventional electronic parameters, viz., HOMO, LUMO, Fermi energy, work function, and energy gap, are also computed, and the effect of an aqueous medium is investigated on these parameters. The ELF and RGD techniques are used to quantify the electron density distribution and explore the involved interactions. TD-DFT calculations are performed to simulate the UV-Vis spectra. Additionally, a quantum theory of atoms in molecules (QTAIM) analysis is conducted, which shows that dopamine@B9N9 and tyramine@B9N9 exhibit stronger hydrogen bond interactions, resulting in increased bond strengths.

Conclusion

B9N9 nanoring shows significant interaction with both neurotransmitters and results in the change in the electronic properties helping in detection of both neurotransmitters.

Graphical Abstract

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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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