揭示多巴胺样分子与Β-Amyloid肽之间的相互作用:一种结合分子动力学和DFT方法。

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Mohammad Erfan Zand, Mohammad Reza Bozorgmehr, Mohammad Momen Heravi, S Ali Beyramabadi
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引用次数: 0

摘要

目的:本研究旨在探讨儿茶酚、多巴胺和左旋多巴对β-淀粉样肽的稳定性和毒性的影响,以评估其作为治疗药物的潜力。β-淀粉样肽在阿尔茨海默病的神经退行性过程中起关键作用。背景:阿尔茨海默病的特征是β-淀粉样肽的聚集,这有助于神经变性。探索各种化合物如何与β-淀粉样肽相互作用可以为潜在的治疗策略提供有价值的见解。目的:探讨儿茶酚、多巴胺和左旋多巴与β-淀粉样肽的相互作用机制,并评估其对肽稳定性和聚集的影响。方法:采用分子动力学模拟结合密度泛函理论研究β-淀粉样蛋白与三种化合物的相互作用。它评估了肽稳定性和盐桥长度的变化,并使用电子定位函数(ELF)和芳香性谐振子模型(HOMA)进行电子结构分析。结果:与儿茶酚相比,β-淀粉样蛋白与多巴胺和左旋多巴相互作用时稳定性明显降低。这三种化合物都能抑制β-淀粉样蛋白,其中多巴胺和左旋多巴的作用更强。儿茶酚主要通过疏水相互作用相互作用,而多巴胺和左旋多巴也与β-淀粉样蛋白形成氢键。电子结构分析表明,儿茶酚具有较高的电子定位和抗芳香性,对其相互作用的影响与多巴胺和左旋多巴不同。从儿茶酚到左旋多巴再到多巴胺的HOMO-LUMO间隙减小表明对β-淀粉样蛋白的反应性增加。结论:多巴胺和左旋多巴比儿茶酚更有效地破坏β-淀粉样蛋白聚集,可能是由于额外的氢键和增加的电子反应性。这些见解对于开发针对阿尔茨海默病β-淀粉样蛋白聚集的治疗策略至关重要,强调了分子相互作用在调节肽稳定性和毒性中的重要性。该研究还提供了化合物的电子性质和相互作用动力学的比较分析,可以指导未来β-淀粉样蛋白抑制剂的设计研究。先进模拟技术的应用强调了计算方法在理解复杂生物相互作用和开发新型治疗剂方面的潜力。此外,对疏水相互作用与氢键的差异效应的见解为旨在减轻β-淀粉样蛋白毒性的新化合物的合成提供了有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling the Interplay between Dopamine-like Molecules and Β-Amyloid Peptide: A Combined Molecular Dynamic and DFT Approach.

Aims: This study aims explore the impact of catechol, dopamine, and L-DOPA on the stability and toxicity of β-amyloid peptides, which play a key role in the neurodegenerative process of Alzheimer's disease, to assess their potential as therapeutic agents.

Background: Alzheimer's disease is marked by the aggregation of β-amyloid peptides, which contribute to neurodegeneration. Exploring how various compounds interact with β-amyloid peptides can offer valuable insights into potential therapeutic strategies.

Objective: The objective of this research is to explore the interaction mechanisms of catechol, dopamine, and L-DOPA with β-amyloid peptides and assess their impact on peptide stability and aggregation.

Method: This study employs molecular dynamics simulations combined with density functional theory to investigate the interactions between β-amyloid and the three compounds. It evaluates changes in peptide stability and salt bridge lengths and performs electronic structure analyses using the Electron Localization Function (ELF) and Harmonic Oscillator Model of Aromaticity (HOMA).

Results: The findings reveal that β-amyloid stability decreases significantly when interacting with dopamine and L-DOPA compared to catechol. All three compounds inhibit β-amyloid, with dopamine and L-DOPA showing stronger effects. Catechol primarily interacts through hydrophobic interactions, while dopamine and L-DOPA also form hydrogen bonds with β-amyloid. Electronic structure analysis shows catechol has higher electron localization and anti-aromatic character, affecting its interactions differently than dopamine and L-DOPA. A decrease in the HOMO-LUMO gap from catechol to L-DOPA to dopamine indicates increasing reactivity towards β-amyloid.

Conclusion: Dopamine and L-DOPA more effectively disrupt β-amyloid aggregation than catechol, likely due to additional hydrogen bonding and increased electronic reactivity. These insights are crucial for developing therapeutic strategies targeting β-amyloid aggregation in Alzheimer's disease, emphasizing the importance of molecular interactions in modulating peptide stability and toxicity. The study also provides a comparative analysis of the electronic properties and interaction dynamics of the compounds, which can guide future research in the design of β-amyloid inhibitors. The utilization of advanced simulation techniques underscores the potential for computational methods in understanding complex biological interactions and developing novel therapeutic agents. Furthermore, the insights into the differential effects of hydrophobic interactions versus hydrogen bonding offer valuable information for the synthesis of new compounds aimed at mitigating β-amyloid toxicity.

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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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