Journal of molecular graphics & modelling最新文献_第2页

ClathPLM: Deep multi-view feature extraction with CNN and attention enhances clathrin protein identification ClathPLM：基于CNN和注意力的深度多视图特征提取增强了网格蛋白的识别。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-21 DOI: 10.1016/j.jmgm.2026.109307

Shuxin Song, Yusen Su, Qingyang Guo, Taigang Liu

Clathrin is a key structural protein in intracellular vesicle transport, mainly mediating clathrin-mediated endocytosis (CME) through trimeric assembly. Its functional abnormalities are closely associated with various diseases, including neurodegenerative disorders, tumor metastasis, and immune system dysregulation. Traditional experimental methods for identifying the presence of Clathrin have limitations such as high cost and time consumption. Therefore, it is particularly urgent to develop efficient and reliable computational methods to assist in Clathrin recognition. In this study, we propose a model named ClathPLM, which integrates sequence embeddings from three pre-trained protein language models (PPLMs), i.e., ProtT5, ProtBert, and ESM-3, and performs deep representation learning on each feature through an independent branch composed of a convolutional neural network (CNN) and a multi-head Attention (MHA) mechanism, finally fusing the representations of the three views to accomplish the classification task. To validate the effectiveness of this design, we further examined variants of the fusion strategy and attention mechanism. Evaluation results show that ClathPLM demonstrates excellent overall classification performance and robustness, surpassing current state-of-the-art methods. Moreover, the model performs strongly on an additional case-study dataset and shows good scalability on an extra vesicular transport proteins (VTPs) dataset. We anticipate that ClathPLM may contribute to a deeper understanding of the role of Clathrin in cellular regulation and disease mechanisms, and facilitate future biological studies as well as potential clinical applications.

网格蛋白是细胞内囊泡运输的关键结构蛋白，主要通过三聚体组装介导网格蛋白介导的内吞作用（CME）。其功能异常与多种疾病密切相关，包括神经退行性疾病、肿瘤转移、免疫系统失调等。传统的检测网格蛋白存在的实验方法存在成本高、耗时长等局限性。因此，开发高效可靠的计算方法来辅助网格蛋白识别显得尤为迫切。在本研究中，我们提出了一个名为ClathPLM的模型，该模型整合了ProtT5、ProtBert和ESM-3三个预训练蛋白语言模型（pplm）的序列嵌入，并通过由卷积神经网络（CNN）和头部注意（MHA）机制组成的独立分支对每个特征进行深度表征学习，最终融合三种视图的表征来完成分类任务。为了验证这一设计的有效性，我们进一步研究了融合策略和注意机制的变体。评估结果表明，ClathPLM具有出色的整体分类性能和鲁棒性，超过了目前最先进的方法。此外，该模型在额外的案例研究数据集上表现良好，并在额外的囊泡转运蛋白（vtp）数据集上显示出良好的可扩展性。我们期待ClathPLM可以为更深入地了解Clathrin在细胞调节和疾病机制中的作用做出贡献，并促进未来的生物学研究和潜在的临床应用。

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引用次数: 0

Integrated experimental and DFT study of dielectric relaxation and optical properties in the chlorobenzene–n-butyl alcohol 氯苯正丁醇介电弛豫和光学性质的综合实验和DFT研究

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-21 DOI: 10.1016/j.jmgm.2026.109305

Samir Azizov , Vusala Nabi Jafarova , Khayala Ajdar Hasanova

An integrated experimental and first-principles investigation was carried out to elucidate the dielectric relaxation and optical properties of the chlorobenzene–n-butyl alcohol binary system containing 0.25 vol fraction of alcohol. The real (ε′) and imaginary (ε″) parts of the complex dielectric permittivity were measured over microwave wavelengths λ = 2.14–37.9 cm and radio frequencies of 0.047–15 MHz in a wide temperature range from −40 °C to 100 °C. The dielectric spectra in the liquid phase reveal two well-defined Debye-type relaxation regions, which are assigned to the independent reorientation of chlorobenzene and n-butyl alcohol molecules, as well as an asymmetric dispersion near the liquidus–solidus interval that is satisfactorily described by the Davidson–Cole model and attributed to hydrogen-bonded alcohol clusters. These features indicate pronounced microheterogeneity and cluster formation governed by hydrogen bonding. Complementary density functional theory (DFT) calculations were performed to provide atomistic insight into the polarization mechanisms and electronic structure of the chlorobenzene–n-butyl alcohol complex. Optical properties were systematically investigated using both the GGA-PBE approach and the more advanced LDA-RPA framework, allowing assessment of polarization screening and collective excitation effects. The calculations predict a wide band gap of about 4–4.5 eV and reveal strong optical anisotropy in the dielectric function, refractive index, optical conductivity, reflectivity, and absorption spectra, with the dominant response along the hydrogen-bonded molecular axis. The onset of intense π→π∗ and n→σ∗ electronic transitions above 4 eV in both GGA-PBE and LDA-RPA spectra is consistent with the experimentally observed dielectric relaxation and absorption behavior. The close agreement between experimental dielectric measurements and DFT-based optical responses confirms that hydrogen-bond-assisted dipole alignment governs both dielectric relaxation and optical polarization. As a result, the chlorobenzene–n-butyl alcohol system can be classified as a low-loss, wide-band-gap dielectric material, making it a promising candidate for microwave resonators, optoelectronic coatings, and polarization-sensitive sensing applications.

采用实验和第一性原理相结合的方法，研究了含0.25 vol分数醇的氯苯-正丁醇二元体系的介电弛豫和光学性质。复介电常数的实部（ε′）和虚部（ε″）在微波波长λ = 2.14-37.9 cm和射频0.047-15 MHz范围内测量，温度范围为- 40°C至100°C。液相中的介电光谱显示了两个定义明确的debye型弛豫区，这两个弛豫区被分配给氯苯和正丁醇分子的独立重定向，以及在液相-固相区间附近的不对称弥散，这是由戴维森-科尔模型满意地描述的，归因于氢键醇簇。这些特征表明明显的微观非均质性和由氢键控制的团簇形成。利用互补密度泛函理论（DFT）计算了氯苯-正丁醇配合物的极化机制和电子结构。使用GGA-PBE方法和更先进的LDA-RPA框架系统地研究了光学性质，从而评估了偏振筛选和集体激发效应。计算结果表明，该材料具有约4-4.5 eV的宽带隙，在介电函数、折射率、光导率、反射率和吸收光谱等方面具有较强的光学各向异性，且主要沿氢键分子轴方向响应。在GGA-PBE和LDA-RPA光谱中，在4 eV以上发生π→π∗和n→σ∗的强烈电子跃迁与实验观察到的介电弛豫和吸收行为一致。实验电介质测量与基于dft的光学响应之间的密切一致证实了氢键辅助偶极子排列控制着介电弛豫和光偏振。因此，氯苯-正丁醇体系可以被归类为一种低损耗、宽带隙的介电材料，使其成为微波谐振器、光电涂层和偏振敏感传感应用的有前途的候选者。

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引用次数: 0

Green chemistry strategies for CO2 valorization: Impact of substituents, solvents and catalysts on the cycloaddition of CO2 with epoxides forming cyclic carbonates 二氧化碳增值的绿色化学策略：取代基、溶剂和催化剂对二氧化碳与环氧化物形成环状碳酸盐的环加成的影响。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-21 DOI: 10.1016/j.jmgm.2026.109306

Imen Ferchichi , Frédéric Guégan , Muneerah Mogren Al-Mogren , Majdi Hochlaf , Youssef Arfaoui

A theoretical study of the reaction forming a cyclic carbonate from the cycloaddition of CO₂ with a typical substituted epoxide, was carried out using first principles methodologies. As epoxide, we considered trans 1-(4-methoxyphenyl)-2-methoxy epoxyethane, exhibiting an optimal reactivity for cycloaddition with CO₂. We found that this reaction exhibits a high energy barrier in the gas phase, thus requiring an activation method. We also investigated the role of various substituted epoxides (mono- and disubstituted) and solvent types, including nonpolar, polar protic, and polar aprotic solvents. Our findings revealed a preference for polar protic solvents, particularly water. Besides, we used catalysts, including Lewis bases and Lewis acids, to selectively favor one reaction pathway over the other. In sum, this study provides deep insights into the factors influencing the reactivity of this cycloaddition and identifies the most suitable solvents and catalysts to promote the reaction, with potential implications for the development of more efficient CO₂ valorization processes.

采用第一性原理方法，对CO2与典型取代环氧化物环加成生成环状碳酸盐的反应进行了理论研究。作为环氧化物，我们考虑了反式1-（4-甲氧基苯基）-2-甲氧基环氧乙烷，它与CO2的环加成反应活性最佳。我们发现该反应在气相中表现出高能量势垒，因此需要激活方法。我们还研究了各种取代环氧化物（单取代和双取代）和溶剂类型的作用，包括非极性，极性质子和极性非质子溶剂。我们的发现揭示了极性质子溶剂的偏好，特别是水。此外，我们还使用了催化剂，包括路易斯碱和路易斯酸，来选择性地促进一种反应途径。综上所述，本研究深入了解了影响该环加成反应活性的因素，并确定了最适合促进该反应的溶剂和催化剂，对开发更高效的CO2增值工艺具有潜在的指导意义。

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引用次数: 0

Boron and nitrogen-doped coronene as high-performance sensors for gamma-hydroxybutyrate drug sensing: A DFT/TD-DFT study 硼和氮掺杂冠烯作为γ -羟基丁酸盐药物传感的高性能传感器：DFT/TD-DFT研究

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-20 DOI: 10.1016/j.jmgm.2026.109299

Mubarak A. Alamri , Mohamed Enneiymy , Yassine Riadi , Ali Altharawi , Taibah Aldakhil , Bharath Kumar Chagaleti , Ali Oubella , Reda A. Haggam

Gamma-hydroxybutyrate (GHB) is a psychoactive compound of high clinical and forensic concern due to its involvement in drug-facilitated intoxications and its rapid metabolic clearance, which complicates reliable detection. The reliance of conventional analytical techniques on centralized laboratories and costly instrumentation highlights the urgent need for fast, sensitive, and on-site sensing platforms. While carbon nanomaterial-based sensors have been widely investigated for narcotics detection, the sensing potential of coronene remains largely unknown. In this work, we present the first systematic DFT and TD-DFT investigation of pristine coronene (Cor) and its boron- and nitrogen-doped derivatives (B-Cor and N-Cor) for GHB detection, revealing a clear dopant-dependent sensing functionality. B-Cor emerges as the most effective GHB adsorbent, exhibiting a strong adsorption energy of −13.52 kcal mol⁻¹, a large increase in dipole moment from 0.24 to 2.07 Debye, and enhanced polarizability (290.04–332.70 a.u.). These effects lead to an exceptional bathochromic shift in the UV–Vis spectrum (λ_max from 373 to 594 nm, Δλ = 221 nm) and a decrease in exciton energy from 3.3 to 2.1 eV, establishing B-Cor as a highly promising single-use adsorptive and colorimetric sensor for naked-eye GHB detection. In contrast, N-Cor is identified as an optimal electrochemical sensor, characterized by a dramatically reduced HOMO-LUMO gap (0.57 eV), high chemical softness (1.75 eV^-1), elevated electrical conductivity (2.75 × 10⁹ A.m⁻²), and an ultra-fast recovery time of 5.63 × 10⁻⁶ s, enabling rapid and reusable sensing. Moderate increases in dipole moment (0.21–0.72 Debye) and polarizability (283.06–325.85 a.u.) further support its strong electronic responsiveness upon GHB binding. By distinctly identifying B-Cor as a superior adsorbent and colorimetric sensor and N-Cor as an efficient electrochemical sensor, this study introduces coronene as a versatile and tunable sensing scaffold and provides a robust theoretical foundation for the rational design of next-generation GHB sensing platforms for forensic and clinical applications.

γ -羟基丁酸酯（GHB）是一种精神活性化合物，由于其参与药物介导的中毒和其快速代谢清除，这使得可靠的检测变得复杂，因此在临床和法医方面受到高度关注。传统分析技术对集中实验室和昂贵仪器的依赖突出了对快速、敏感和现场传感平台的迫切需求。虽然基于碳纳米材料的传感器已被广泛研究用于毒品检测，但冠烯的传感潜力在很大程度上仍然未知。在这项工作中，我们首次对原始冕烯（Cor）及其硼和氮掺杂衍生物（B-Cor和N-Cor）进行了系统的DFT和TD-DFT研究，用于GHB检测，揭示了明确的掺杂依赖传感功能。B-Cor是最有效的GHB吸附剂，吸附能为- 13.52 kcal mol - 1，偶极矩从0.24增加到2.07 Debye，极化率提高（290.04-332.70 a.u）。这些效应导致UV-Vis光谱的显色偏移（λmax从373 nm到594 nm， Δλ = 221 nm）和激子能量从3.3 eV降低到2.1 eV，使B-Cor成为一种非常有前途的一次性吸附和比色传感器，用于裸眼GHB检测。相比之下，N-Cor被认为是一种最佳的电化学传感器，其特点是大大减小了HOMO-LUMO间隙（0.57 eV），高化学柔软度（1.75 eV-1），提高了电导率（2.75 × 109 am - 2），超快的恢复时间为5.63 × 10−6 s，能够实现快速和可重复使用的传感。偶极矩（0.21-0.72 Debye）和极化率（283.06-325.85 a.u）的适度增加进一步支持了其对GHB结合的强电子响应性。通过明确确定B-Cor是一种优越的吸附剂和比色传感器，N-Cor是一种高效的电化学传感器，本研究介绍了冠烯作为一种多功能和可调的传感支架，为合理设计用于法医和临床应用的下一代GHB传感平台提供了坚实的理论基础。

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引用次数: 0

Exploring the structural basis of organic compounds by predicting experimental IR peaks: a machine learning analysis 通过预测实验红外峰来探索有机化合物的结构基础：机器学习分析。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-20 DOI: 10.1016/j.jmgm.2026.109304

Sadaf Noreen , Mamduh J. Aljaafreh , Ashour M. Ahmed

To understand the structural foundation of organic compounds is crucial in fields like chemistry and materials science. This study is a machine learning quest for predicting the experimental carbonyl peaks in the infrared (IR) spectrum of organic compounds from Modred and RDKit descriptors. The results show that FractionCSP3 is the most correlating descriptor for both types of descriptors. The Extra Trees (ET) regression yields the best performance with its coefficient of determination (R²) of 0.72–0.78. The analysis of models with SHapley Additive exPlanations (SHAP) shows that BCUT2D_MRLOW (RDKit) and FCSP3 (Modred) are the most influential descriptors. Hyperparameter tuning with 50 estimators optimizes model performance. Additionally, the calculated synthetic accessibility (SA) scores have 0.00–0.15 range to provide insights into the feasibility of synthesis. The current findings demonstrate the power of machine learning in uncovering the structural basis of organic compounds and predicting their experimental IR peaks.

了解有机化合物的结构基础在化学和材料科学等领域是至关重要的。本研究是一项机器学习探索，用于预测来自Modred和RDKit描述符的有机化合物红外（IR）光谱中的实验羰基峰。结果表明，对于这两种类型的描述符，FractionCSP3是最相关的描述符。额外树（Extra Trees， ET）回归的决定系数（R2）为0.72 ~ 0.78，效果最好。SHapley加性解释（SHAP）模型分析表明，BCUT2D_MRLOW （RDKit）和FCSP3 （Modred）是影响最大的描述子。使用50个估计器的超参数调优优化了模型性能。此外，计算出的合成可达性（SA）分数范围在0.00-0.15之间，可以深入了解合成的可行性。目前的发现证明了机器学习在揭示有机化合物的结构基础和预测它们的实验红外峰方面的力量。

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引用次数: 0

Dynamic consequences of threonine mutations in the CaLM motif of RET/GFRα1/GDNF ternary complex RET/GFRα1/GDNF三元配合物CaLM基序苏氨酸突变的动态影响

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-19 DOI: 10.1016/j.jmgm.2026.109301

Bithia R., George Priya Doss C.

The extracellular domain (ECD) of the RET receptor tyrosine kinase depends on its cysteine-rich domain (CRD) for calcium coordination, structural stability, and assembly with GFRα1 and GDNF. Mutations close to the CRD CaLM motif have been associated with disease, but their molecular effects remain understudied. In this study, we analyzed two clinically reported variants, T564N and T564P, using all-atom molecular dynamics simulations of both the isolated CRD and the RET/GFRα1/GDNF ternary complex. Our analysis showed that both the mutations introduced localized structural changes in the CRD monomer. T564N caused increased residue fluctuations at the mutation site and solvent exposure, whereas T564P enhanced flexibility across all calcium-coordinating residues and slightly decreased stabilizing contacts. These effects became more noticeable in the ternary complex. Within the complex, interactions with the neighbouring domains caused the CRD to adopt conformations that compensated for the structural changes observed in the CRD monomer. In this context, each mutation affected calcium-binding energetics differently, resulting in more favourable binding in the mutants than in the wild-type. Although calcium binding was energetically favourable, the overall interaction energy within the complex was still affected. The complex highlighted mutation-specific differences in RET's interactions with GFRα1 and GDNF. The comparison between monomeric and complex simulations indicates that the functional impact of T564 mutations cannot be inferred from the isolated CRD. Together, these results show that the structural and energetic consequences of CRD CaLM mutations depend strongly on the full signaling assembly. This underscores the need to assess RET variants within their native multiprotein environment to understand how disease-associated mutations may alter receptor function.

RET受体酪氨酸激酶的胞外结构域（ECD）依赖于其富含半胱氨酸的结构域（CRD）来进行钙的配位、结构稳定性以及与GFRα1和GDNF的组装。接近CRD - CaLM基序的突变与疾病有关，但其分子效应仍未得到充分研究。在这项研究中，我们分析了两个临床报道的变异，T564N和T564P，使用分离的CRD和RET/GFRα1/GDNF三元配合物的全原子分子动力学模拟。我们的分析表明，这两种突变都在CRD单体中引入了局部结构变化。T564N增加了突变位点和溶剂暴露的残基波动，而T564P增强了所有钙配位残基的灵活性，并略微减少了稳定接触。这些效应在三元配合物中更加明显。在复合物内，与邻近结构域的相互作用导致CRD采用构象，以补偿在CRD单体中观察到的结构变化。在这种情况下，每个突变对钙结合能量的影响不同，导致突变体比野生型更有利于钙结合。虽然钙结合在能量上是有利的，但复合物内的总相互作用能仍然受到影响。该复合体突出了RET与GFRα1和GDNF相互作用的突变特异性差异。单体和复杂模拟的比较表明，T564突变的功能影响不能从分离的CRD中推断出来。总之，这些结果表明，CRD - CaLM突变的结构和能量后果强烈依赖于完整的信号组装。这强调了在其原生多蛋白环境中评估RET变异体以了解疾病相关突变如何改变受体功能的必要性。

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引用次数: 0

DeepHybridCPI: A hybrid deep learning framework for compound–protein interaction prediction DeepHybridCPI：用于化合物-蛋白质相互作用预测的混合深度学习框架。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-19 DOI: 10.1016/j.jmgm.2026.109303

Areen Rasool, Jamshaid Ul Rahman, Qasim Ali

In bioinformatics, deep learning-based methods for Compound-Protein Interaction (CPI) prediction play a vital role in virtual screening, drug discovery, and drug repositioning. Recent improvements in computational methods have shown great possibility to save costs of experiment and speed up target identification. Nevertheless, the current CPI forecasting methods remain severely limited. Many rely on shallow Graph Neural Networks (GNNs) that struggle to capture the global structural context of compounds, while conventional Convolutional Neural Networks (CNNs) focus primarily on local sequence motifs and fail to model long-range dependencies in proteins. Even though a number of recent architectures strive to solve these problems by adding complexity to models, or by adding complex modules, these additions often cause significant computational overhead. To overcome these challenges, we propose DeepHybridCPI, a hybrid deep learning framework designed for accurate and efficient CPI prediction. Our hybrid model integrates a multiscale, densely connected GNN to extract compound features capturing both local substructures and global molecular topology, and employs CNNs with Long Short-Term Memory (LSTM) networks to model both local motifs and extended dependencies in protein sequences. The learned compound and protein representations are fused into a unified latent space to enable effective interaction modeling. Experimental evaluations on benchmark Human and C. elegans datasets demonstrate that DeepHybridCPI consistently outperforms existing state-of-the-art baseline methods in terms of AUC, Precision, and Recall. These findings highlight the importance of combining multiscale compound representations with hybrid sequence encoders within a single unified framework, providing a promising avenue for accelerating computational drug discovery. We release our source code and dataset at: https://github.com/jamshaidwarraich/DeepHybridCPI.

在生物信息学中，基于深度学习的化合物-蛋白质相互作用（CPI）预测方法在虚拟筛选、药物发现和药物重新定位中发挥着至关重要的作用。近年来计算方法的改进显示出节省实验成本和加快目标识别速度的巨大可能性。然而，目前的CPI预测方法仍然有很大的局限性。许多人依赖于浅层图神经网络（gnn），难以捕捉化合物的全局结构背景，而传统的卷积神经网络（cnn）主要关注局部序列基序，无法模拟蛋白质的长期依赖关系。尽管许多最新的体系结构通过向模型中添加复杂性或添加复杂模块来努力解决这些问题，但这些添加通常会导致显著的计算开销。为了克服这些挑战，我们提出了DeepHybridCPI，这是一个混合深度学习框架，旨在准确有效地预测CPI。我们的混合模型集成了一个多尺度、密集连接的GNN来提取捕获局部子结构和全局分子拓扑的化合物特征，并使用具有长短期记忆（LSTM）网络的cnn来建模蛋白质序列中的局部基元和扩展依赖。学习到的化合物和蛋白质表示被融合到一个统一的潜在空间中，以实现有效的相互作用建模。对基准人类和秀丽隐杆线虫数据集的实验评估表明，DeepHybridCPI在AUC、Precision和Recall方面始终优于现有的最先进的基线方法。这些发现强调了在单一统一框架内将多尺度化合物表示与混合序列编码器相结合的重要性，为加速计算药物发现提供了一条有希望的途径。我们在https://github.com/jamshaidwarraich/DeepHybridCPI上发布了源代码和数据集。

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引用次数: 0

Molecular mechanisms of aquaporin 1 inhibition by Bacopaside I and Bacopaside II: Insights from molecular dynamics simulations Bacopaside I和Bacopaside II抑制水通道蛋白1的分子机制：来自分子动力学模拟的见解。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-19 DOI: 10.1016/j.jmgm.2026.109302

Mingyu Wei , Zien Yu , Chi Huang , Shuwei Han , Sitian Zhou , Ting Wang , Shibin Chang , Xiaoying Chen , Feisheng Zhong , Shaojie Ma

Aquaporin-1 (AQP1),a key water channel protein, is aberrantly overexpressed in multiple malignancies, rendering it a compelling therapeutic target. The natural products Bacopaside I and Bacopaside II have demonstrated inhibitory activity against AQP1, yet their molecular mechanisms remain elusive. To elucidate the atomic basis of this inhibition, we employed a comprehensive computational approach combining molecular docking, molecular dynamics (MD) simulations, and extensive Gaussian accelerated molecular dynamics (GaMD) simulations with molecular mechanics generalized Born surface area (MM/GBSA) analysis. Our simulations indicate that both compounds exert spatial effects by occupying pore space, physically blocking channels, and forming van der Waals interactions with hydrophobic amino acids. In addition, ligands form hydrogen bonds with amino acids near these regions, resulting in narrower channels compared to other parts of AQP1. MM/GBSA calculations indicate that Bacopaside Ⅱ (ΔG_bind = −34.48 kcal/mol) has a higher binding affinity than Bacopaside I (ΔG_bind = −31.76 kcal/mol). Energy decomposition analysis identifies key interacting residues Pro171, Ile174, and Ala66 that anchor the inhibitors. Although both ligands induce subtle constriction of the channel pores, Bacopaside II establishes a more persistent hydrogen bonding network, underscoring its unique energetic contribution to the inhibition profile. Overall, these findings provide a detailed mechanistic blueprint for AQP1 inhibition by Bacopasides and offer a structural framework for the rational design of next-generation AQP1-targeted anticancer therapies.

水通道蛋白-1 （AQP1）是一种关键的水通道蛋白，在多种恶性肿瘤中异常过表达，使其成为一个引人注目的治疗靶点。天然产物bacop皂苷I和bacop皂苷II已显示出对AQP1的抑制活性，但其分子机制尚不清楚。为了阐明这种抑制作用的原子基础，我们采用了综合计算方法，结合分子对接、分子动力学（MD）模拟、广泛的高斯加速分子动力学（GaMD）模拟和分子力学广义Born表面积（MM/GBSA）分析。我们的模拟表明，这两种化合物通过占据孔隙空间、物理阻塞通道和与疏水氨基酸形成范德华相互作用来发挥空间效应。此外，配体与这些区域附近的氨基酸形成氢键，导致与AQP1的其他部分相比通道更窄。MM/GBSA计算表明，BacopasideⅡ（ΔGbind = -34.48 kcal/mol）比Bacopaside I （ΔGbind = -31.76 kcal/mol）具有更高的结合亲和力。能量分解分析确定了锚定抑制剂的关键相互作用残基Pro171， Ile174和Ala66。尽管这两种配体都诱导了通道孔的细微收缩，但Bacopaside II建立了一个更持久的氢键网络，强调了它对抑制谱的独特能量贡献。总之，这些发现为Bacopasides抑制AQP1提供了详细的机制蓝图，并为合理设计下一代AQP1靶向抗癌疗法提供了结构框架。

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引用次数: 0

Quantum chemical investigations of electronic properties, chemical reactivity, FTIR, thermodynamic behaviors and biological activity for some novel quercetin derivatives 一些新型槲皮素衍生物的电子性质、化学反应性、红外光谱、热力学行为和生物活性的量子化学研究。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-17 DOI: 10.1016/j.jmgm.2026.109298

Noha A. Saleh , Bushra M.D. Mansoor , A. Alkhaldi , Munirah A. Almessiere

Quercetin is a crucial natural bioactive substance present in many plants offering numerous health benefits. This computational study uses DFT calculations at the B3LYP/6-31G∗∗ level to computationally investigate novel quercetin derivatives modified by a tetra-amino acid sequence (Ser-Gly-Lys-Arg) in various structural forms including α-amino acids, β-amino acids, mixed-amino acids, and ketoacid. These modifications aim to improve the physicochemical and biological properties of quercetin providing a helpful framework for producing more potent bioactive compounds. Frontier molecular orbital analysis, molecular electrostatic potential mapping and reactivity indices reveal that β-amino acid and ketoacid derivatives exhibit enhanced electronic reactivity and thermodynamic stability relative to native quercetin. In addition, quantitative structure activity relationship (QSAR) descriptors and ADMET predictions are employed as comparative indicators to assess trends in polarity, solubility, and molecular permeability among the investigated compounds. The results highlight how peptide conjugation modulates the electronic and physicochemical properties of quercetin providing a rational computational basis for the future design of quercetin based peptidomimetic systems for further experimental investigation.

槲皮素是一种重要的天然生物活性物质，存在于许多植物中，对健康有许多好处。本计算研究使用B3LYP/6-31G * *水平的DFT计算来计算研究由四氨基酸序列（Ser-Gly-Lys-Arg）修饰的新型槲皮素衍生物的各种结构形式，包括α-氨基酸、β-氨基酸、混合氨基酸和酮酸。这些修饰旨在改善槲皮素的物理化学和生物学特性，为生产更有效的生物活性化合物提供有益的框架。前沿分子轨道分析、分子静电势作图和反应性指数表明，β-氨基酸和酮酸衍生物的电子反应性和热力学稳定性均优于天然槲皮素。此外，定量结构活性关系（QSAR）描述符和ADMET预测被用作比较指标，以评估所研究化合物之间的极性、溶解度和分子渗透性的趋势。研究结果揭示了肽偶联如何调节槲皮素的电子和物理化学性质，为未来设计基于槲皮素的拟肽系统提供了合理的计算基础，并进行了进一步的实验研究。

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引用次数: 0

Molecular dynamic simulation elucidates temperature- and pH-dependent film-forming mechanisms of soybean β-conglycinin and glycinin 分子动力学模拟阐明了大豆β-甘氨酸和甘氨酸的温度和ph依赖性成膜机制。

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-16 DOI: 10.1016/j.jmgm.2026.109297

Meiling Liu , Yuan Zhao , Linfan Shi , Zhongyang Ren , Wuyin Weng , Meitian Xiao

The effects of temperature (25–95 °C) and pH (7.0–9.0) on the film-forming mechanisms of soybean β-conglycinin (7S) and glycinin (11S) were investigated using molecular dynamics (MD) simulations and experimental validation. All MD simulations achieved equilibrium within 50 ns. The elevated temperatures and lower pH conditions reduced the center-of-mass distance and the radius-of-gyration (Rg) between 7S and 11S, while increasing hydrogen bond formation and binding free energy. Solvent-accessible surface area decreased with temperature, while root-mean-square fluctuation remained stable at pH 7.0 but increased with temperature at pH 9.0. The 7S-11S films prepared at higher temperatures exhibited enhanced tensile strength and higher proportion of hydrophobic interactions. With increasing temperature of the 7S-11S solution, the elongation at break increased at pH 7.0, but initially increased and then decreased at pH 9.0. Fourier transform infrared spectra revealed that hydrogen bonds and β-sheet structures increased with increasing temperature. In conclusion, heating the film-forming solution at pH 7.0 promoted 7S-11S molecular interactions, thereby improving the mechanical properties.

采用分子动力学（MD）模拟和实验验证的方法，研究了温度（25 ~ 95℃）和pH（7.0 ~ 9.0）对大豆β-甘氨酸（7S）和甘氨酸（11S）成膜机理的影响。所有MD模拟均在50 ns内达到平衡。温度升高和pH降低降低了7S ~ 11S之间的质心距离和旋转半径（Rg），同时增加了氢键形成和结合自由能。溶剂可及表面积随温度升高而减小，均方根波动在pH 7.0时保持稳定，但在pH 9.0时随温度升高而增大。在较高温度下制备的7S-11S薄膜具有更高的拉伸强度和更高的疏水相互作用比例。随着7S-11S溶液温度的升高，断裂伸长率在pH 7.0时升高，但在pH 9.0时先升高后降低。傅里叶红外光谱显示，随着温度的升高，氢键和β-片结构增加。综上所述，在pH 7.0下加热成膜液促进了7S-11S分子间的相互作用，从而改善了材料的力学性能。

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