Exploring Metformin's Therapeutic Potential for Alzheimer's Disease: An In-Silico Perspective Using Well-Tempered Funnel Metadynamics.

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2025-04-13 DOI:10.1021/acs.jcim.5c00106
Sunandini Swain,Atanu K Metya
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Abstract

Alzheimer's disease (AD), often referred to as the "diabetes of the brain", is intricately linked to insulin resistance. Metformin, a first-line antidiabetic drug, has been anticipated as a potential treatment for AD and is currently undergoing phase 3 clinical trials. The potential success of metformin in treating AD could herald a new era in the management of this debilitating disease, providing hope for millions of people affected worldwide. Despite this fact, the precise molecular mechanisms underlying the therapeutic effects of metformin on AD remain poorly understood. To pursue this, in this present work, we implement a comprehensive computational approach combining classical molecular dynamics (MD) simulations and the advanced enhanced sampling technique funnel metadynamics (FM) to explore the dynamics and affinity of metformin and acetylcholinesterase (AChE), a novel target for AD. The MD and FM simulations suggest that metformin induces significant configurational changes within the AChE, resulting in weak and nonspecific binding. Furthermore, the presence of metformin alters the conformational landscape of AChE causing the emergence of metastable states and less rigid binding patterns. The binding energies for the metformin-AChE complex are -4.89 ± 1.2 kcal/mol and -1.68 ± 0.2 kcal/mol, as estimated through the molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and FM approaches, respectively. To elucidate the binding energy relevance calculated by MMPBSA and FM approach with experimental inhibitory potency, ΔGexp is calculated using IC50 value reported in prior experimental studies. ΔGexp is estimated to be -3.59 kcal/mol. A comparison of these binding energy values with different methods highlights the moderate inhibitory potency of metformin toward AChE. This work provides molecular-level insights emphasizing the dynamic configurational changes induced by metformin within AChE and underscores its translational potential in the repurposing of AD.
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探索二甲双胍治疗阿尔茨海默病的潜力:利用井喷式漏斗元动力学的室内视角。
阿尔茨海默病(AD),通常被称为“大脑的糖尿病”,与胰岛素抵抗有着复杂的联系。二甲双胍(Metformin)是一种一线降糖药物,有望成为阿尔茨海默病(AD)的潜在治疗药物,目前正在进行三期临床试验。二甲双胍治疗阿尔茨海默病的潜在成功可能预示着这种使人衰弱的疾病管理的新时代,为全世界数百万受影响的人带来希望。尽管如此,二甲双胍治疗AD的确切分子机制仍然知之甚少。为了实现这一目标,在本工作中,我们实施了一种综合计算方法,结合经典分子动力学(MD)模拟和先进的增强采样技术漏斗元动力学(FM)来探索二甲双胍和乙酰胆碱酯酶(AChE)的动力学和亲和力,AChE是AD的新靶点。MD和FM模拟表明,二甲双胍在乙酰胆碱酯酶内引起显著的构型变化,导致弱和非特异性结合。此外,二甲双胍的存在改变了乙酰胆碱酯的构象景观,导致亚稳态的出现和更少的刚性结合模式。通过分子力学泊松-玻尔兹曼表面积法(MMPBSA)和FM法估算,二甲双胍-乙酰氨基乙酸配合物的结合能分别为-4.89±1.2 kcal/mol和-1.68±0.2 kcal/mol。为了阐明MMPBSA和FM方法计算的结合能与实验抑制效力的相关性,使用先前实验研究报道的IC50值计算ΔGexp。ΔGexp估计为-3.59千卡/摩尔。用不同的方法比较这些结合能值,表明二甲双胍对乙酰胆碱的抑制作用是中等的。这项工作提供了分子水平的见解,强调了二甲双胍在乙酰胆碱内引起的动态构型变化,并强调了其在AD重新利用中的转化潜力。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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