非侵入性乳腺癌检测:利用DFT理论利用bn掺杂C60杂富勒烯的甲醛传感潜力

IF 3.1 4区 生物学 Q2 BIOLOGY
Bharath Kumar Chagaleti , Arafat Toghan , Magdi E.A. Zaki , Ali Oubella , Reda A. Haggam
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引用次数: 0

摘要

乳腺癌仍然是妇女死亡的主要原因,因此有必要发展非侵入性诊断方法。甲醛(FA)已成为尿液中早期检测乳腺癌的潜在生物标志物。本研究利用密度泛函理论(DFT)探讨了硼氮掺杂C60杂富勒烯(BN(5,6)C58和BN(6,6)C58)作为高灵敏度和选择性生物传感器用于FA检测的有效性。一套综合的电子、热力学和量子化学描述符被用来评估传感电位。关键计算参数(包括显著减小的能差(HLG = 0.49 eV)、高吸附能(Eads =−12.55 kcal/mol)、有利的吉布斯自由能变化(ΔG =−12.73 kcal/mol)、增强的偶极矩(μ = 7.425 D)、增加的极化率(α = 525.640)和非共价相互作用(NCI)分析)共同证实了BN掺杂显著增强了与FA的相互作用强度,其中BN(6,6)C58表现出最高的敏感性(1.9 ×1017)。电子性质分析表明,BN(6,6)C58@FA的能隙减小,电荷转移增强,分子静电势和NCI分析证实了这一点。该传感器的快速恢复时间(1.65 ×10−3 s)和高导电性(16 a.m.−2)进一步强调了其实时呼吸分析的潜力。这些发现突出了BN(6,6)C58是非侵入性乳腺癌诊断的有希望的候选者,为开发先进的电化学生物传感器铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-invasive breast cancer detection: Leveraging the potential of BN-doped C60 heterofullerene for formaldehyde sensing using DFT theory
Breast cancer remains a leading cause of mortality among women, necessitating the development of non-invasive diagnostic methods. Formaldehyde (FA) has emerged as a potential biomarker for early detection of breast cancer in urine. This study explores the efficacy of boron-nitrogen-doped C60 heterofullerenes (BN(5,6)C58 and BN(6,6)C58) as highly sensitive and selective biosensors for FA detection using density functional theory (DFT). A comprehensive set of electronic, thermodynamic, and quantum chemical descriptors was employed to evaluate the sensing potential. Key computed parameters (including a significantly reduced energy gap (HLG = 0.49 eV), a high adsorption energy (Eads = −12.55 kcal/mol), a favorable Gibbs free energy change (ΔG = −12.73 kcal/mol), an enhanced dipole moment (μ = 7.425 D), increased polarizability (α = 525.640), and non-covalent interaction (NCI) analysis) collectively confirmed that BN doping significantly enhances the interaction strength with FA, with BN(6,6)C58 exhibiting the highest sensitivity (1.9 ×1017). Electronic property analyses demonstrated a reduced energy gap and enhanced charge transfer in BN(6,6)C58@FA, corroborated by molecular electrostatic potential and NCI analyses. The sensor's rapid recovery time (1.65 ×10−3 s) and high electrical conductivity (16 A.m−2) further underscore its potential for real-time breath analysis. These findings highlight BN(6,6)C58 as a promising candidate for non-invasive breast cancer diagnostics, paving the way for developing advanced electrochemical biosensors.
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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