Journal of molecular graphics & modelling最新文献

Identification of molecular compounds targeting bacterial propionate metabolism with topological machine learning 基于拓扑机器学习的细菌丙酸代谢分子化合物鉴定

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-15 DOI: 10.1016/j.jmgm.2026.109287

Astrit Tola , Shan Aziz , Dannie Zhabilov , Duane Winkler , Mehmet Candas , Baris Coskunuzer

This study demonstrates the transformative potential of machine learning in drug discovery by integrating comparative protein and ligand analysis with novel topological machine learning methods. Our approach sifts through large chemical libraries to identify promising molecular structures for targeting specific proteins with high precision. While many machine learning models have proven effective on benchmark datasets, we apply these techniques to discover compounds targeting methylcitrate dehydratase (AcnD), the second enzyme in the bacterial propionate catabolism pathway. Propionate catabolism is essential in pathogenic bacteria for utilizing host derived lipids and amino acids. Inefficient removal of propionate can lead to toxic accumulation that threatens bacterial survival, making this pathway a potential antimicrobial target. We translate ligand molecular structures into topological vectors and use tailored topological models to prioritize compounds with characteristics consistent with blocking the AcnD active site. Molecular docking simulations indicate that prioritized compounds interact with key amino acid residues critical to AcnD function. Among these, 2-methylidenebutanedioic acid (itaconic acid, itaconate) ranks highly as a potential molecular scaffold for targeting AcnD. Using bacterial growth assays, we find that itaconate at 29.13 mM completely inhibits the growth of Pseudomonas aeruginosa and Acinetobacter baumannii in carbon rich liquid cultures. These findings reinforce itaconate’s potential as an antimicrobial metabolite and support the hypothesis that it can disrupt bacterial propionate catabolism, potentially by inhibiting AcnD and promoting the accumulation of toxic intermediates. Overall, our study underscores the value of integrating topology based ligand modeling with comparative sequence structure function analysis and docking to identify molecular scaffolds with favorable geometric fit, energy, and interaction profiles, guiding downstream optimization and experimental validation. Our code is available at (https://github.com/AstritTola/Molecular-Compounds-Targeting).

本研究通过将比较蛋白质和配体分析与新型拓扑机器学习方法相结合，展示了机器学习在药物发现方面的变革潜力。我们的方法通过大型化学文库筛选，以高精度地确定靶向特定蛋白质的有前途的分子结构。虽然许多机器学习模型已被证明在基准数据集上是有效的，但我们应用这些技术来发现靶向甲基柠檬酸脱水酶（AcnD）的化合物，AcnD是细菌丙酸分解代谢途径中的第二种酶。丙酸分解代谢是致病菌利用宿主来源的脂质和氨基酸所必需的。丙酸盐的低效去除可导致毒性积聚，威胁细菌的生存，使该途径成为潜在的抗菌靶点。我们将配体分子结构翻译成拓扑载体，并使用定制的拓扑模型来优先考虑与阻断AcnD活性位点一致的化合物。分子对接模拟表明，优先化合物与AcnD功能的关键氨基酸残基相互作用。其中，2-甲基二丁二酸（衣康酸，衣康酸酯）被认为是一种潜在的靶向AcnD的分子支架。通过细菌生长试验，我们发现衣康酸在29.13 mM处完全抑制铜绿假单胞菌和鲍曼不动杆菌在富碳液体培养中的生长。这些发现加强了衣康酸作为抗菌代谢物的潜力，并支持了衣康酸可能通过抑制AcnD和促进有毒中间体积累来破坏细菌丙酸分解代谢的假设。总的来说，我们的研究强调了将基于拓扑的配体建模与比较序列结构功能分析和对接相结合的价值，以识别具有良好几何拟合，能量和相互作用特征的分子支架，指导下游优化和实验验证。我们的代码可在（https://github.com/AstritTola/Molecular-Compounds-Targeting）上获得。

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引用次数: 0

On QSPR analysis for predicting efficacy of physicochemical properties of antibiotics drugs via topological indices and regression models 基于拓扑指数和回归模型预测抗菌药物理化性质疗效的QSPR分析

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-13 DOI: 10.1016/j.jmgm.2026.109280

Yingxuan Huang , W. Eltayeb Ahmed , Muhammad Farhan Hanif , Saba Hanif , Muhammad Imran , Muhammad Kamran Siddiqui

Quantitative structure property relationship(QSPR) has emerged as an indispensable tool for the estimation of physicochemical properties in drug molecules using mathematical and computational methods. Here, we introduce novel reverse degree based topological indices to see their applicability in case of selected antibiotic compounds property prediction. Reliable models to predict properties such as the boiling point, molar refractivity and enthalpy of vaporization exist to correlate molecular structure with experimentally reported physicochemical parameters. We have analyzed structurally different antibiotics with regression models developed in Python and SPSS in order to guarantee the robustness and reproducibility. We note here that predictive measures of cubic regression models seem to perform better, as observed through generally greater correlation coefficients. The results show that the reverse topological indices are efficient for recording structural differences in antibiotic molecules and they can be excellent descriptors for predicting their physical and chemical properties. It also stresses that, the use of reverse degree based descriptors on antibiotic compounds is new, providing a basis for further QSPR modeling for more general drug families. This work is part of a growing trend to study the interfaces between graph theory and cheminformatics where new indices help to improve our understanding over molecular properties with importance for drug design.

定量结构性质关系（QSPR）已成为利用数学和计算方法估计药物分子物理化学性质的重要工具。在这里，我们引入了新的基于逆度的拓扑指标，以观察它们在选定抗生素化合物性质预测中的适用性。存在可靠的模型来预测诸如沸点、摩尔折射率和汽化焓等性质，从而将分子结构与实验报告的物理化学参数联系起来。为了保证稳健性和可重复性，我们使用Python和SPSS开发的回归模型对结构不同的抗生素进行分析。我们在这里注意到，三次回归模型的预测措施似乎表现得更好，正如通过普遍较大的相关系数观察到的那样。结果表明，反向拓扑指数可以有效地记录抗生素分子的结构差异，并可作为预测其物理和化学性质的良好描述符。它还强调，在抗生素化合物上使用基于反向度的描述符是新的，为进一步对更一般的药物家族进行QSPR建模提供了基础。这项工作是研究图论和化学信息学之间接口的一个日益增长的趋势的一部分，其中新的指标有助于提高我们对分子特性的理解，对药物设计具有重要意义。

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引用次数: 0

A first-principles study of hydrogen storage on MXene Mo2C monolayer MXene Mo2C单层储氢的第一性原理研究

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-12 DOI: 10.1016/j.jmgm.2026.109283

Mohamed Bakhit, Sina Karimzadeh, Tien-Chien Jen

This study investigates the potential of Mo₂C MXene as a hydrogen storage material using density functional theory (DFT) and molecular dynamics (MD) simulations to examine its structural stability, electronic properties, and hydrogen adsorption behavior. The optimized Mo₂C structure exhibits a hexagonal lattice with favorable adsorption sites over Mo atoms and shows a surface area expansion of approximately 4 % after hydrogen loading while maintaining lattice symmetry. Thermodynamic stability is confirmed through adsorption energy calculations, which reveal a clear relationship between energy levels and hydrogen concentration. The results indicate that H₂ adsorption on Mo₂C is a thermodynamically favorable and exothermic process, with adsorption energies ranging from −0.184 to −0.528 eV, satisfying the criteria for practical hydrogen storage applications. Charge transfer analysis identifies Mo atoms as electron acceptors. Density of States (DOS) calculations reveal a near-zero band gap, confirming the metallic nature of Mo₂C, while Projected DOS (PDOS) and orbital maps show significant hybridization and electronic polarization among H, Mo, and C atoms. Charge density difference maps highlight effective charge redistribution with strong electric fields around Mo atoms. MD simulations further confirm the structural stability of the Mo₂C–H₂ system, showing minimal deformation during a 100 ps simulation and supporting efficient hydrogen adsorption. Overall, these findings establish Mo₂C MXene as a promising candidate for hydrogen storage applications and provide valuable insights for experimental validation and further development of sustainable energy storage technologies.

本研究利用密度泛函理论（DFT）和分子动力学（MD）模拟研究了Mo2C MXene作为储氢材料的潜力，以研究其结构稳定性、电子性能和氢吸附行为。优化后的Mo2C结构呈现六边形晶格，具有对Mo原子有利的吸附位点，并且在保持晶格对称性的情况下，氢负载后表面面积扩大了约4%。通过吸附能计算证实了热力学稳定性，揭示了能级与氢浓度之间的明确关系。结果表明，H2在Mo2C上的吸附是一个热力学有利的放热过程，吸附能在−0.184 ~−0.528 eV之间，满足实际储氢应用的标准。电荷转移分析确定Mo原子为电子受体。态密度（DOS）计算显示Mo2C的带隙接近于零，证实了Mo2C的金属性质，而投影态密度（PDOS）和轨道图显示H、Mo和C原子之间存在明显的杂化和电子极化。电荷密度差图突出了Mo原子周围强电场下的有效电荷重分布。MD模拟进一步证实了Mo2C-H2体系的结构稳定性，在100 ps的模拟中显示出最小的变形，并支持高效的氢吸附。总的来说，这些发现确立了Mo2C MXene作为储氢应用的有前途的候选者，并为实验验证和可持续储能技术的进一步发展提供了有价值的见解。

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引用次数: 0

Exploring traditional Chinese medicine for antiviral drug discovery: A computational approach to combat human metapneumovirus (HMPV) 利用中药开发抗病毒药物：一种对抗人偏肺病毒（HMPV）的计算方法

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-12 DOI: 10.1016/j.jmgm.2026.109290

Amit Dubey , Manish Kumar , Aisha Tufail , Vivek Dhar Dwivedi

Human metapneumovirus (HMPV) remains a major respiratory pathogen without approved antivirals, highlighting the urgent need for novel therapeutics. This study implemented an integrative computational pipeline combining virtual screening, molecular docking, 2 μs molecular dynamics (MD) simulations, density functional theory (DFT), pharmacophore modeling, and ADMET profiling to identify potent HMPV inhibitors from Traditional Chinese Medicine. Among 180 screened phytoconstituents, glycyrrhizin (–9.3 kcal mol⁻¹), hesperidin (–9.1 kcal mol⁻¹), and saikosaponins (–9.0 kcal mol⁻¹) exhibited strong binding affinities toward the HMPV matrix protein (PDB ID: 5WB0). Extended MD simulations confirmed complex stability with RMSD 0.17–0.22 nm, average of 3–5 persistent H-bonds, and DCCM correlation coefficient = 0.86 for glycyrrhizin. MM-PBSA binding free energies (ΔG_bind) of –46.2 ± 2.5, –44.7 ± 2.8, and –43.9 ± 2.2 kJ mol⁻¹ for glycyrrhizin, hesperidin, and oseltamivir respectively, validated strong and stable interactions. DFT results indicated favorable electronic reactivity (HOMO–LUMO gap = 3.86 eV; electrophilicity = 2.74 eV), enhancing ligand-target complementarity. ADMET analysis predicted low systemic toxicity (LD₅₀ = 380–530 mg kg⁻¹) but revealed moderate CYP3A4/CYP2C9 inhibition, suggesting the need for metabolic stability evaluation. Compared with reported fusion inhibitors such as EGCG and rutin, this matrix-targeted strategy introduces a distinct therapeutic mechanism. Overall, these findings establish a robust computational foundation for developing and experimentally validating potent natural inhibitors against HMPV.

人偏肺病毒（HMPV）仍然是一种主要的呼吸道病原体，尚未获得批准的抗病毒药物，这表明迫切需要新的治疗方法。本研究采用虚拟筛选、分子对接、2 μs分子动力学（MD）模拟、密度泛函数理论（DFT）、药效团建模和ADMET谱分析相结合的综合计算流程，鉴定中药中有效的HMPV抑制剂。在筛选的180种植物成分中，甘草酸（-9.3 kcal mol−1）、橙皮苷（-9.1 kcal mol−1）和柴草皂苷（-9.0 kcal mol−1）与HMPV基质蛋白（PDB ID: 5WB0）具有较强的结合亲和力。扩展的MD模拟证实了甘草酸配合物的稳定性，RMSD为0.17-0.22 nm，平均有3-5个持久氢键，DCCM相关系数为0.86。甘草酸、橙皮苷和奥司他韦的MM-PBSA结合自由能（ΔG_bind）分别为-46.2±2.5、-44.7±2.8和-43.9±2.2 kJ mol−1，证实了强而稳定的相互作用。DFT结果表明，良好的电子反应性（HOMO-LUMO间隙= 3.86 eV，亲电性= 2.74 eV），增强了配体-靶标的互补性。ADMET分析预测低全身毒性（LD50 = 380-530 mg kg - 1），但显示中度CYP3A4/CYP2C9抑制，提示需要代谢稳定性评估。与已有报道的融合抑制剂如EGCG和芦丁相比，这种基质靶向策略引入了一种独特的治疗机制。总的来说，这些发现为开发和实验验证抗HMPV的有效天然抑制剂建立了强大的计算基础。

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引用次数: 0

Y- and Zr-modified boron nitride nanosheets as efficient sensors for formamide: A first-principles approach Y和zr修饰的氮化硼纳米片作为甲酰胺的高效传感器：第一性原理方法

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-12 DOI: 10.1016/j.jmgm.2026.109278

Meryem Derdare, Abdel-Ghani Boudjahem, Nedjoua Cheghib

This study employs DFT calculations to investigate the structural stability and electronic properties of pristine and transition-metal-doped boron nitride (BN) nanosheets, using yttrium (Y) and zirconium (Zr) as dopants, as well as their gas-sensing response toward formamide (FO). The findings show that introducing Y or Zr atoms leads to notable modifications in the electronic structure of the BN nanosheet, substantially improving its chemical reactivity and adsorption performance. In the aqueous phase, the interaction between FO and Y/Zr-doped BN nanosheets becomes moderately weaker, with adsorption energies decreasing to – 4.23 to – 24.97 kcal mol⁻¹; however, the most stable complexes still exhibit comparatively strong binding. Solvation also alters the electronic structure of the nanosheets, leading to noticeable variations in their energy gaps. Despite this reduction in interaction strength, both doped materials retain high sensitivity toward FO in water, with ZrBN reaching 99.9 %/1.43 × 10₃ % and YBN achieving 55.9 %/86.5 %. Moreover, the nanosheets exhibit extremely short recovery times in the liquid phase, with values of 1.27 × 10⁻¹⁵ s for ZrBN and 2.06 s for YBN, enabling rapid FO desorption and efficient restoration of active metal sites. These combined features confirm the strong potential of Y- and Zr-doped BN nanosheets as reusable and high-performance sensors for formamide detection in aqueous environments.

本研究采用DFT计算研究了原始和过渡金属掺杂的氮化硼（BN）纳米片的结构稳定性和电子性能，使用钇(Y)和锆（Zr）作为掺杂剂，以及它们对甲酰胺（FO）的气敏响应。研究结果表明，引入Y或Zr原子可以显著改变BN纳米片的电子结构，显著提高其化学反应性和吸附性能。在水相中，FO与掺杂Y/ zr的BN纳米片的相互作用变弱，吸附能降至- 4.23 ~ - 24.97 kcal mol−1；然而，最稳定的配合物仍然表现出相对强的结合。溶剂化也会改变纳米片的电子结构，导致其能隙的显著变化。尽管相互作用强度降低，但两种掺杂材料对水中FO的灵敏度都很高，ZrBN达到99.9% /1.43 × 103%， YBN达到55.9% / 86.5%。此外，纳米片在液相中表现出极短的恢复时间，ZrBN的值为1.27 × 10−15 s， YBN的值为2.06 s，能够快速解吸FO并有效恢复活性金属位。这些综合特性证实了Y和zr掺杂BN纳米片作为可重复使用的高性能传感器在水环境中检测甲酰胺的强大潜力。

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引用次数: 0

Structure-based discovery of novel TAOK3 inhibitor via virtual screening, molecular dynamics simulations, and MM/GBSA analysis 通过虚拟筛选、分子动力学模拟和MM/GBSA分析，发现基于结构的新型TAOK3抑制剂

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-12 DOI: 10.1016/j.jmgm.2026.109286

Ali M. Alaseem , Glowi Alasiri , Mohamed M. El-Wekil , Al-Montaser Bellah H. Ali , Ahmed K. Hamdy

Cancer persists as a leading cause of global mortality, and the mitogen-activated protein kinase (MAPK) pathway plays a pivotal role in tumor progression and drug resistance. Among MAPK regulators, TAOK3 has emerged as a promising therapeutic target due to its oncogenic role in various cancers. Despite its significance, no clinically approved TAOK3 inhibitors exist. In this study we implemented a structure-based virtual screening approach to identify potential TAOK3 inhibitors from a library of 10,000 lead-like compounds. Molecular docking identified ten top-ranked candidates, with compound Z1 (ZINC ID: 77585305) demonstrating the strongest binding affinity (ΔG = −8.42 kcal/mol), outperforming reported inhibitors NCGC00188382 and SBI-581. ADMET profiling confirmed Z1's favorable drug-like properties, including high gastrointestinal absorption and minimal toxicity risks. Molecular dynamics simulations (100 ns) confirmed stable binding of Z1 to TAOK3, as indicated by low RMSD (<0.25 nm), consistent RMSF profiles, and compact radius of gyration. End-state free energy calculations using MM/GBSA also supported favorable binding, with Z1 showing excellent van der Waals interactions (−39.82 kcal/mol). Dynamic cross-correlation matrices and free energy landscape analysis further validated the stability of the TAOK3-Z1 complex. Collectively, these findings highlight Z1 as a promising TAOK3 inhibitor and a potential lead compound for further experimental validation in anticancer drug development.

癌症一直是全球死亡的主要原因，而丝裂原活化蛋白激酶（MAPK）途径在肿瘤进展和耐药性中起着关键作用。在MAPK调节因子中，TAOK3由于其在多种癌症中的致癌作用而成为一个有希望的治疗靶点。尽管具有重要意义，但尚未有临床批准的TAOK3抑制剂存在。在这项研究中，我们实施了一种基于结构的虚拟筛选方法，从10,000种铅样化合物的文库中鉴定潜在的TAOK3抑制剂。分子对接鉴定出10个排名前十位的候选化合物，其中化合物Z1（锌ID: 77585305）表现出最强的结合亲和力（ΔG =−8.42 kcal/mol），优于已报道的抑制剂NCGC00188382和SBI-581。ADMET分析证实了Z1有利的药物样特性，包括高胃肠道吸收和最小的毒性风险。分子动力学模拟（100 ns）证实了Z1与TAOK3的稳定结合，显示出低RMSD （<0.25 nm）、一致的RMSF分布和紧凑的旋转半径。用MM/GBSA计算的终态自由能也支持良好的结合，Z1表现出良好的范德华相互作用（- 39.82 kcal/mol）。动态互相关矩阵和自由能景观分析进一步验证了TAOK3-Z1配合物的稳定性。总的来说，这些发现突出了Z1作为一种有前途的TAOK3抑制剂和潜在的先导化合物在抗癌药物开发中的进一步实验验证。

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引用次数: 0

RLBindDeep: A ResNet-LSTM based novel framework for protein–ligand binding affinity prediction RLBindDeep：一个基于ResNet-LSTM的蛋白质配体结合亲和力预测新框架

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-12 DOI: 10.1016/j.jmgm.2026.109282

Ekarsi Lodh , Shalini Majumder , Tapan Chowdhury , Manashi De

The prediction of the binding affinity of proteins and ligands in computational drug discovery with high accuracy is critical when evaluating the effectiveness of potential therapeutic compounds. This research work introduces RLBindDeep, a novel deep learning architecture based on the amalgamation of the ResNet and LSTM architectures, for improved accuracy in predicting protein–ligand binding affinities. Most traditional methodologies utilizing conventional molecular docking techniques suffer from poor accuracy owing to semi-flexible modeling approaches and limited considerations of complex interactions. On the other hand, RLBindDeep, which is formulated as a pose-independent binding affinity regression model that directly predicts experimental protein–ligand binding affinities from fixed complex structures, without performing docking or rescoring multiple poses, has performed well in extracting important features of the protein–ligand interaction. Specifically, the extracted features encompass ligand physicochemical descriptors (e.g., molecular weight, LogP, TPSA), protein-level features such as amino acid composition, and detailed interaction features including van der Waals, electrostatic, and hydrogen-bond energies. The model has been tested rigorously over the CASF-2016 benchmark dataset and has returned Pearson’s coefficient

R = 0.875

, Spearman’s coefficient

ρ = 0.864

, and Root Mean Square Error

RMSE = 0.993

. This significantly outperforms existing state-of-the-art models, such as HAC-Net and AutoDock Vina. Improved accuracy and robustness in RLBindDeep further highlight the possibility of deep learning to revolutionize computational drug discovery processes, making strategies for drug development more efficient and targeted.

在计算药物发现中，高精度地预测蛋白质和配体的结合亲和力对于评估潜在治疗化合物的有效性至关重要。本研究引入了RLBindDeep，这是一种基于ResNet和LSTM架构融合的新型深度学习架构，用于提高预测蛋白质-配体结合亲和力的准确性。由于半灵活的建模方法和对复杂相互作用的考虑有限，大多数利用传统分子对接技术的传统方法精度较差。另一方面，RLBindDeep是一种不依赖于姿态的结合亲和力回归模型，它可以直接预测固定复杂结构中实验蛋白与配体的结合亲和力，而不需要进行对接或重新记录多个姿态，在提取蛋白质与配体相互作用的重要特征方面表现良好。具体来说，提取的特征包括配体的物理化学描述符（如分子量、LogP、TPSA）、蛋白质水平特征（如氨基酸组成）和详细的相互作用特征（包括范德华、静电和氢键能）。该模型已在CASF-2016基准数据集上进行了严格测试，并返回Pearson系数R=0.875， Spearman系数ρ=0.864，均方根误差RMSE=0.993。这明显优于现有的最先进的模型，如HAC-Net和AutoDock Vina。RLBindDeep提高了准确性和鲁棒性，进一步强调了深度学习在彻底改变计算药物发现过程中的可能性，使药物开发策略更高效、更有针对性。

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引用次数: 0

Discovery of a novel PI3Kα inhibitor for breast cancer therapy via virtual screening method, molecular dynamics simulation and biological evaluation 通过虚拟筛选方法、分子动力学模拟和生物学评价发现一种新的乳腺癌治疗PI3Kα抑制剂

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-12 DOI: 10.1016/j.jmgm.2026.109289

Thitiya Boonma , Bodee Nutho , Phongthon Kanjanasirirat , Chananya Rajchakom , Nadtanet Nunthaboot

Phosphatidylinositol-4,5-bisphosphate 3-kinase alpha (PI3Kα) is a central signaling enzyme driving cell proliferation and growth in cancers including breast cancer. Selective inhibition of PI3Kα isoform has become a promising therapeutic approach. In this work, 2000 in-house natural compounds were virtually screened against the ATP-binding site of PI3Kα. Of these, 618 compounds were predicted to have acceptable drug-likeness, pharmacokinetic, and toxicity properties based on in silico ADMET screening. Docking analysis highlighted four candidates forming stable hydrogen bonds with key residues V851, S854, and Q859 in the PI3Kα binding pocket. Molecular dynamics simulations were then used to assess their structural features and dynamic stability. Hit 2 was found to form strong hydrogen bonds with E849 and V851 of the PI3Kα protein. MM/GBSA-based binding free energy analysis supported that Hit 2 possessed the most favorable binding affinity to PI3Kα among the identified candidates. In vitro cytotoxicity assays were then performed in MCF-7 and MDA-MB-231 breast cancer cell lines, with alpelisib as a reference compound. Hit 2 reduced cell viability in both cell lines, but its effect was particularly pronounced in MDA-MB-231 cells, a model of triple-negative breast cancer (TNBC). These results suggest that Hit 2 represents a promising natural scaffold for further design and development in breast cancer therapy, with particular relevance for aggressive TNBC.

磷脂酰肌醇-4,5-二磷酸3-激酶α (PI3Kα)是驱动包括乳腺癌在内的癌症细胞增殖和生长的中心信号酶。选择性抑制PI3Kα异构体已成为一种很有前途的治疗方法。在这项工作中，2000种内部天然化合物对PI3Kα的atp结合位点进行了虚拟筛选。其中，618种化合物预测具有可接受的药物相似性、药代动力学和基于计算机ADMET筛选的毒性。对接分析显示，在PI3Kα结合口袋中，有4个候选分子与关键残基V851、S854和Q859形成稳定的氢键。然后用分子动力学模拟来评估它们的结构特征和动态稳定性。Hit 2与PI3Kα蛋白的E849和V851形成强氢键。基于MM/ gbsa的结合自由能分析结果表明，Hit 2与PI3Kα的结合亲和力最强。然后在MCF-7和MDA-MB-231乳腺癌细胞系中进行体外细胞毒性测定，以alpelisib为参比化合物。Hit 2降低了两种细胞系的细胞活力，但其作用在三阴性乳腺癌（TNBC）模型MDA-MB-231细胞中尤为明显。这些结果表明，Hit 2代表了一种有前途的天然支架，可以进一步设计和开发乳腺癌治疗，特别是与侵袭性TNBC相关。

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引用次数: 0

Molecular simulations of the tunable pore structure models elucidate the adsorption of sulfamethoxazole on biochar 可调孔结构模型的分子模拟阐明了磺胺甲恶唑在生物炭上的吸附

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-10 DOI: 10.1016/j.jmgm.2026.109276

Zehui Zhang, Hong Wei, Feng Pan, Ruijie Teng, Junqi Song, Shujie Xie

Biochar is an environmentally friendly adsorption material that can effectively adsorb sulfamethoxazole (SMX) in water. However, the relationship between the pore structure characteristics of biochar and the SMX adsorption process remains unclear. In this study, molecular dynamics (MD) simulations combined with complementary experiments was employed to investigate SMX adsorption on four biochar models: isolated mesopores (BC1), micropores (BC2), hierarchical pores (BC3), and amorphous carbon (BC4). A remarkable concordance was observed between the MD simulations and experimental results. MD simulations revealed that porous structures facilitate SMX adsorption, leading to the fastest adsorption equilibrium rate for BC4. Additionally, a correlation between pore size and the SMX adsorption kinetics was observed. Within micropores, SMX achieves adsorption equilibrium at a slower rate, exhibiting a diffusion coefficient 62 % lower than that observed in mesopores. Noncovalent interaction (NCI) analysis and energy decomposition demonstrated that both π-π interactions and hydrogen bonds jointly stabilize SMX adsorption on biochar, with van der Waals interaction (contributing 59 %) playing a dominant role. Experimental results showed that the adsorption of SMX onto BBC-800 conformed to the Langmuir isotherm and pseudo-second-order kinetic model. Both simulations and experiments jointly elucidated the adsorption mechanism of SMX onto biochar, primarily involving pore filling, π-π stacking, and hydrogen bonding interactions. These findings provide atomic-scale insights for designing biochar with optimized pore structures for antibiotic removal.

生物炭是一种能有效吸附水中磺胺甲恶唑（SMX）的环保型吸附材料。然而，生物炭的孔隙结构特征与SMX吸附过程之间的关系尚不清楚。本研究采用分子动力学（MD）模拟和互补实验相结合的方法，研究了SMX在分离介孔（BC1）、微孔（BC2）、分层孔（BC3）和无定形碳（BC4）四种生物炭模型上的吸附。模拟结果与实验结果有显著的一致性。MD模拟表明，多孔结构有利于SMX的吸附，导致BC4的吸附平衡速率最快。此外，还观察到孔径大小与SMX吸附动力学之间的相关性。在微孔中，SMX以较慢的速率达到吸附平衡，其扩散系数比在中孔中低62%。非共价相互作用（NCI）分析和能量分解表明，π-π相互作用和氢键共同稳定了SMX在生物炭上的吸附，其中范德华相互作用（贡献59%）起主导作用。实验结果表明，SMX在bc -800上的吸附符合Langmuir等温线和拟二级动力学模型。模拟和实验共同阐明了SMX在生物炭上的吸附机理，主要涉及孔隙填充、π-π堆积和氢键相互作用。这些发现为设计具有优化孔结构的生物炭去除抗生素提供了原子尺度的见解。

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引用次数: 0

Computational modeling of ubiquitin specific protease 7 (USP7) complexes with N-benzylpiperidinol derivatives incorporating binding site flexibility 结合结合位点灵活性的n -苄基胡椒醇衍生物泛素特异性蛋白酶7 （USP7）配合物的计算建模

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling

Pub Date : 2026-01-08 DOI: 10.1016/j.jmgm.2025.109272

Jorge Luis Valdés-Albuernes, Erbio Díaz-Pico, José Luis Velázquez-Libera, Julio Caballero

Ubiquitin-specific protease 7 (USP7) is a key regulator of protein homeostasis, playing critical roles in various cellular processes, including DNA damage response, immune signaling, and oncogenesis. Targeting USP7 with small-molecule inhibitors has emerged as a promising therapeutic strategy, particularly in the context of cancer and autoimmune diseases. Among the diverse scaffolds explored for USP7 inhibition, N-benzylpiperidinol (NBP) derivatives have shown notable potential due to their structural versatility and bioactivity. Computationally, it is possible to access models of complexes between these inhibitors and USP7 by utilizing the crystallographic structures of USP7 available in the Protein Data Bank. In a classical approach, models of NBPs can be obtained within a rigid USP7 structure. In this work, we report models of complexes between 58 NBPs and variable conformations of USP7 using a flexible docking protocol employing the novel CorrEA method. As part of this protocol, we obtained diverse USP7 structures through molecular dynamics (MD) and selected complex models with inhibitors based on their biological activities. Model quality was validated using LigRMSD and interaction fingerprints (IFP). The flexible treatment of USP7 enabled the capture of binding-site conformational changes. These changes are critical for explaining the activity differences among the studied compounds.

泛素特异性蛋白酶7 （USP7）是蛋白质稳态的关键调节因子，在多种细胞过程中发挥关键作用，包括DNA损伤反应、免疫信号传导和肿瘤发生。用小分子抑制剂靶向USP7已成为一种有前景的治疗策略，特别是在癌症和自身免疫性疾病的背景下。在多种抑制USP7的支架中，n -苄基胡椒醇（NBP）衍生物由于其结构的通用性和生物活性而显示出显着的潜力。通过计算，利用蛋白质数据库中可用的USP7的晶体结构，可以获得这些抑制剂和USP7之间的复合物模型。在经典方法中，NBPs的模型可以在刚性USP7结构中获得。在这项工作中，我们报告了58个NBPs和USP7的可变构象之间的复合物模型，使用了一种灵活的对接协议，采用了新的CorrEA方法。作为该方案的一部分，我们通过分子动力学（MD）获得了不同的USP7结构，并根据其生物活性选择了具有抑制剂的复杂模型。采用LigRMSD和交互指纹（IFP）对模型质量进行验证。对USP7的灵活处理可以捕获结合位点的构象变化。这些变化对于解释所研究化合物之间的活性差异至关重要。

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引用次数: 0