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3DReact: Geometric Deep Learning for Chemical Reactions. 3DReact:化学反应几何深度学习。
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-15 DOI: 10.1021/acs.jcim.4c00104
Puck van Gerwen, Ksenia R Briling, Charlotte Bunne, Vignesh Ram Somnath, Ruben Laplaza, Andreas Krause, Clemence Corminboeuf

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

几何深度学习模型将相关的分子对称性纳入神经网络架构,大大提高了分子性质预测的准确性和数据效率。在这一成功的基础上,我们引入了 3DReact 这一几何深度学习模型,通过反应物和生成物的三维结构预测反应特性。我们证明,该模型的不变版本足以应对现有的反应数据集。我们在 GDB7-22-TS、Cyclo-23-TS 和 Proparg-21-TS 数据集上展示了该模型在不同原子映射机制下预测活化势垒的竞争性能。我们发现,与现有的反应性质预测模型相比,3DReact 提供了一个灵活的框架,可以利用原子映射信息(如果有的话)以及反应物和生成物的几何形状(以不变或等变的方式)。因此,在不同的数据集、原子映射机制以及内插法和外推法任务中,3DReact 的系统性能都很好。
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引用次数: 0
Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals. 从结构到特性:预测晶体电子特性的化学元素嵌入。
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-15 DOI: 10.1021/acs.jcim.3c01990
Shokirbek Shermukhamedov, Dilorom Mamurjonova, Thana Maihom, Michael Probst

We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic representations that enable it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gaps exceeds results previously published. By design, our model is not restricted to the electronic properties discussed here but can be extended to fit diverse chemical descriptors. Its advantages are (a) its low computational requirements, making it an efficient tool for high-throughput screening of materials; and (b) the simplicity and flexibility of its architecture, facilitating implementation and interpretation, especially for researchers in the field of computational chemistry.

我们介绍了一种新的通用机器学习模型,它能够预测各种晶体特性,包括费米级能量和带隙,以及光谱特性(如电子态密度)。该模型基于原子表征,能有效捕捉晶体中每个原子及其周围环境的复杂信息。带隙的精确度超过了之前公布的结果。根据设计,我们的模型并不局限于本文讨论的电子特性,而是可以扩展到适合各种化学描述符。它的优势在于:(a)计算要求低,是高通量筛选材料的有效工具;(b)结构简单灵活,便于实施和解释,尤其适合计算化学领域的研究人员。
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引用次数: 0
Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment 在隐含溶剂环境中精确建模 RNA 干环
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-13 DOI: 10.1021/acs.jcim.4c00756
Jason T. Linzer, Ethan Aminov, Aalim S. Abdullah, Colleen E. Kirkup, Rebeca I. Diaz Ventura, Vinay R. Bijoor, Jiyun Jung, Sophie Huang, Chi Gee Tse, Emily Álvarez Toucet, Hugo P. Onghai, Arghya P. Ghosh, Alex C. Grodzki, Emilee R. Haines, Aditya S. Iyer, Mark K. Khalil, Alexander P. Leong, Michael A. Neuhaus, Joseph Park, Asir Shahid, Matthew Xie, Jan M. Ziembicki, Carlos Simmerling, Maria C. Nagan
Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.
核糖核酸(RNA)分子在溶液中可以采用多种二级和三级结构,其中茎环是比较常见的结构之一。在此,我们对在隐含溶剂环境中用分子动力学模拟的 15 个 RNA 干环序列进行了系统分析。对 RNA 簇集合的分析表明,茎环结构一般能在茎部区域采用 A 型 RNA。环状结构更为敏感,只有修改力场中的 CH-O 相互作用,并结合隐式溶剂非极性校正以更好地模拟碱基堆叠相互作用,才能再现实验结构。用目前基于原子物理学的模型精确建模 RNA 仍然具有挑战性,但本文研究的 RNA 系统可以为将来测试其他 RNA 建模方法提供有用的基准集。
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引用次数: 0
Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions. 为立体可控反应寻找相关的逆合成断裂。
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-12 DOI: 10.1021/acs.jcim.4c00370
Olaf Wiest, Christoph Bauer, Paul Helquist, Per-Ola Norrby, Samuel Genheden

Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.

机器学习驱动的计算机辅助合成规划(CASP)工具已成为复杂分子合成设计中生成想法的重要工具,但却不能充分解决目标化合物的立体化学特征问题。免费提供的 AiZynthFinder 软件采用一种新方法自动提取 CASP 中使用的模板,其中包括美国专利商标局 (USPTO) 和阿斯利康内部数据库中的立体化学信息,后者包含来自 Reaxys、Pistachio 和阿斯利康电子实验笔记本的反应。从美国专利商标局提取了 367 个模板,涵盖试剂和底物控制以及立体特异性反应,而从阿斯利康数据库提取了 20724 个模板。针对 ChEMBL 数据库中的 936 个靶点和内部选择的 791 个 AZ 设计,对这些模板在多步 CASP 中的性能进行了评估。讨论了 ChEMBL 的四个案例研究和 FDA 批准药物的潜力和局限性。
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引用次数: 0
Ligand-Based Virtual Screening for Discovery of Indole Derivatives as Potent DNA Gyrase ATPase Inhibitors Active against Mycobacterium tuberculosis and Hit Validation by Biological Assays. 基于配体的虚拟筛选,以发现对结核分枝杆菌有活性的强效 DNA 回旋酶 ATP 酶抑制剂吲哚衍生物,并通过生物检测验证命中率。
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-12 DOI: 10.1021/acs.jcim.4c00511
Bongkochawan Pakamwong, Paptawan Thongdee, Bundit Kamsri, Naruedon Phusi, Somjintana Taveepanich, Kampanart Chayajarus, Pharit Kamsri, Auradee Punkvang, Supa Hannongbua, Jidapa Sangswan, Khomson Suttisintong, Sanya Sureram, Prasat Kittakoop, Poonpilas Hongmanee, Pitak Santanirand, Jiraporn Leanpolchareanchai, James Spencer, Adrian J Mulholland, Pornpan Pungpo

Mycobacterium tuberculosis is the single most important global infectious disease killer and a World Health Organization critical priority pathogen for development of new antimicrobials. M. tuberculosis DNA gyrase is a validated target for anti-TB agents, but those in current use target DNA breakage-reunion, rather than the ATPase activity of the GyrB subunit. Here, virtual screening, subsequently validated by whole-cell and enzyme inhibition assays, was applied to identify candidate compounds that inhibit M. tuberculosis GyrB ATPase activity from the Specs compound library. This approach yielded six compounds: four carbazole derivatives (1, 2, 3, and 8), the benzoindole derivative 11, and the indole derivative 14. Carbazole derivatives can be considered a new scaffold for M. tuberculosis DNA gyrase ATPase inhibitors. IC50 values of compounds 8, 11, and 14 (0.26, 0.56, and 0.08 μM, respectively) for inhibition of M. tuberculosis DNA gyrase ATPase activity are 5-fold, 2-fold, and 16-fold better than the known DNA gyrase ATPase inhibitor novobiocin. MIC values of these compounds against growth of M. tuberculosis H37Ra are 25.0, 3.1, and 6.2 μg/mL, respectively, superior to novobiocin (MIC > 100.0 μg/mL). Molecular dynamics simulations of models of docked GyrB:inhibitor complexes suggest that hydrogen bond interactions with GyrB Asp79 are crucial for high-affinity binding of compounds 8, 11, and 14 to M. tuberculosis GyrB for inhibition of ATPase activity. These data demonstrate that virtual screening can identify known and new scaffolds that inhibit both M. tuberculosis DNA gyrase ATPase activity in vitro and growth of M. tuberculosis bacteria.

结核分枝杆菌是全球最重要的传染病杀手,也是世界卫生组织优先开发新型抗菌药物的重要病原体。结核杆菌 DNA 回旋酶是抗结核药物的一个有效靶点,但目前使用的抗结核药物针对的是 DNA 断裂重组,而不是 GyrB 亚基的 ATPase 活性。在此,我们采用虚拟筛选方法,随后通过全细胞和酶抑制实验进行验证,从物种化合物库中找出能抑制结核杆菌 GyrB ATPase 活性的候选化合物。这种方法产生了六种化合物:四种咔唑衍生物(1、2、3 和 8)、苯并吲哚衍生物 11 和吲哚衍生物 14。咔唑衍生物可视为结核杆菌 DNA 回旋酶 ATP 酶抑制剂的新支架。化合物 8、11 和 14 抑制结核杆菌 DNA 回旋酶 ATP 酶活性的 IC50 值(分别为 0.26、0.56 和 0.08 μM)分别是已知 DNA 回旋酶 ATP 酶抑制剂新生物素的 5 倍、2 倍和 16 倍。这些化合物对结核杆菌 H37Ra 生长的 MIC 值分别为 25.0、3.1 和 6.2 μg/mL,优于新生物素(MIC > 100.0 μg/mL)。对接的 GyrB:抑制剂复合物模型的分子动力学模拟表明,与 GyrB Asp79 的氢键相互作用是化合物 8、11 和 14 与 M. tuberculosis GyrB 高亲和力结合以抑制 ATPase 活性的关键。这些数据表明,虚拟筛选可以鉴定出既能抑制结核杆菌 DNA 回旋酶 ATPase 体外活性又能抑制结核杆菌生长的已知和新支架。
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引用次数: 0
Molecular Mechanisms Underlying Single Nucleotide Polymorphism-Induced Reactivity Decrease in CYP2D6. 单核苷酸多态性诱导 CYP2D6 反应性降低的分子机制
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-12 DOI: 10.1021/acs.jcim.4c00276
Daniel Becker, Prasad V Bharatam, Holger Gohlke

Cytochrome P450 2D6 (CYP2D6) is one of the most important enzymes involved in drug metabolism. Genetic polymorphism can influence drug metabolism by CYP2D6 such that a therapy is seriously affected by under- or overdosing of drugs. However, a general explanation at the atomistic level for poor activity is missing so far. Here we show for the 20 most common single nucleotide polymorphisms (SNPs) of CYP2D6 that poor metabolism is driven by four mechanisms. We found in extensive all-atom molecular dynamics simulations that the rigidity of the I-helix (central helix), distance between central phenylalanines (stabilizing bound substrate), availability of basic residues on the surface of CYP2D6 (binding of cytochrome P450 reductase), and position of arginine 132 (electron transfer to heme) are essential for an extensive function of the enzyme. These results were applied to SNPs with unknown effects, and potential SNPs that may lead to poor drug metabolism were identified. The revealed molecular mechanisms might be important for other drug-metabolizing cytochrome P450 enzymes.

细胞色素 P450 2D6 (CYP2D6)是参与药物代谢的最重要酶之一。基因多态性会影响 CYP2D6 对药物的代谢,从而导致药物剂量不足或过量,严重影响治疗效果。然而,迄今为止还没有从原子水平上对药物活性低下做出一般性解释。在这里,我们针对 20 种最常见的 CYP2D6 单核苷酸多态性(SNPs)研究发现,代谢不良是由四种机制驱动的。我们在大量的全原子分子动力学模拟中发现,I-螺旋(中心螺旋)的刚性、中心苯丙氨酸之间的距离(稳定结合底物)、CYP2D6 表面碱性残基的可用性(结合细胞色素 P450 还原酶)以及精氨酸 132 的位置(电子传递到血红素)对于酶的广泛功能至关重要。将这些结果应用于未知影响的 SNPs,发现了可能导致药物代谢不良的潜在 SNPs。所揭示的分子机制可能对其他药物代谢细胞色素 P450 酶也很重要。
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引用次数: 0
GOLEM: Automated and Robust Cryo-EM-Guided Ligand Docking with Explicit Water Molecules GOLEM:自动、稳健的低温电子显微镜引导配体与显性水分子对接
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-11 DOI: 10.1021/acs.jcim.4c00917
Zhiyu Zhao, Emad Tajkhorshid
A detailed understanding of ligand–protein interaction is essential for developing rational drug-design strategies. In recent years, technological advances in cryo-electron microscopy (cryo-EM) brought a new era to the structural determination of biological macromolecules and assemblies at high resolution, marking cryo-EM as a promising tool for studying ligand–protein interactions. However, even in high-resolution cryo-EM results, the densities for the bound small-molecule ligands are often of lower quality due to their relatively dynamic and flexible nature, frustrating their accurate coordinate assignment. To address the challenge of ligand modeling in cryo-EM maps, here we report the development of GOLEM (Genetic Optimization of Ligands in Experimental Maps), an automated and robust ligand docking method that predicts a ligand’s pose and conformation in cryo-EM maps. GOLEM employs a Lamarckian genetic algorithm to perform a hybrid global/local search for exploring the ligand’s conformational, orientational, and positional space. As an important feature, GOLEM explicitly considers water molecules and places them at optimal positions and orientations. GOLEM takes into account both molecular energetics and the correlation with the cryo-EM maps in its scoring function to optimally place the ligand. We have validated GOLEM against multiple cryo-EM structures with a wide range of map resolutions and ligand types, returning ligand poses in excellent agreement with the densities. As a VMD plugin, GOLEM is free of charge and accessible to the community. With these features, GOLEM will provide a valuable tool for ligand modeling in cryo-EM efforts toward drug discovery.
详细了解配体与蛋白质之间的相互作用对于制定合理的药物设计策略至关重要。近年来,低温电子显微镜(cryo-EM)技术的发展为高分辨率生物大分子和组装体的结构测定开创了新纪元,标志着低温电子显微镜成为研究配体与蛋白质相互作用的一种前景广阔的工具。然而,即使是高分辨率冷冻电镜结果,由于小分子配体具有相对动态和灵活的特性,其结合的配体密度往往质量较低,从而影响了配体坐标的准确分配。为了解决低温电子显微镜图谱中配体建模的难题,我们在此报告了 GOLEM(实验图谱中配体的遗传优化)的开发情况,这是一种自动、稳健的配体对接方法,可预测低温电子显微镜图谱中配体的姿态和构象。GOLEM 采用拉马克遗传算法进行全局/局部混合搜索,以探索配体的构象、方向和位置空间。GOLEM 的一个重要特点是明确考虑水分子,并将其置于最佳位置和方向。GOLEM 在其评分函数中考虑了分子能量学以及与低温电子显微镜图的相关性,以优化配体的位置。我们已根据多个低温电子显微镜结构对 GOLEM 进行了验证,这些结构具有广泛的图谱分辨率和配体类型,返回的配体位置与密度非常吻合。作为 VMD 插件,GOLEM 是免费的,社区也可以访问。凭借这些功能,GOLEM 将为低温电子显微镜配体建模提供宝贵的工具,从而促进药物发现。
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引用次数: 0
NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation 基于分子动力学模拟分析蛋白质异构相互作用的网络服务器 NRIMD
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-11 DOI: 10.1021/acs.jcim.4c00783
Yi He, Shuang Wang, Shuai Zeng, Jingxuan Zhu, Dong Xu, Weiwei Han, Juexin Wang
Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.
蛋白质中远端位点和活性位点之间的长程异构通讯是生物调控的核心,但其特征仍然不甚明了,限制了蛋白质工程和药物设计的发展。针对这一空白,NRIMD 是一个开放访问的网络服务器,用于通过分子动力学(MD)模拟分析蛋白质中的长程相互作用,如远端位点的突变效应或活性中心上异位点的异位配体结合效应。基于我们最近利用图神经网络进行神经关系推断的研究成果,这个基于云的网络服务器可接受主流 MD 软件提供的任何长度残基的α-碳骨架格式的 MD 模拟数据。输入的轨迹数据在部署在云端的前端进行验证,然后在部署在高性能计算机系统上的后端进行处理,后端配有一系列辅助工具。网络服务器提供了一个一站式 MD 分析平台,用于预测远距离相互作用以及远距离位点和活性位点之间的相互作用路径。它为详细分析和可视化提供了友好的用户界面。据我们所知,NRIMD 是首个在 MD 模拟中提供全面长程相互作用分析的在线服务,大大降低了利用深度学习预测蛋白质长程相互作用的门槛。NRIMD 网络服务器可通过 https://nrimd.luddy.indianapolis.iu.edu/ 公开获取。
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引用次数: 0
Riboflavin-Induced DNA Damage and Anticancer Activity in Breast Cancer Cells under Visible Light: A TD-DFT and In Vitro Study. 可见光下核黄素诱导的乳腺癌细胞 DNA 损伤和抗癌活性:一项 TD-DFT 和体外研究。
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-10 DOI: 10.1021/acs.jcim.4c01104
Ranabir Majumder, Shreya Banerjee, Sayan Paul, Saugat Mondal, Madhurima Mandal, Priya Ghosh, Debjit Maity, Anakuthil Anoop, N D Pradeep Singh, Mahitosh Mandal

Targeted treatments for breast cancer that minimize harm to healthy cells are highly sought after. Our study explores the potentiality of riboflavin as a targeted anticancer compound that can be activated by light irradiation. Here, we integrated time-dependent density functional theory (TD-DFT) calculations and an in vitro study under visible light. The TD-DFT calculations revealed that the electronic charge transferred from the DNA base to riboflavin, with the most significant excitation peak occurring within the visible light range. Guided by these insights, an in vitro study was conducted on the breast cancer cell lines MCF-7 and MDA-MB-231. The results revealed substantial growth inhibition in these cell lines when exposed to riboflavin under visible light, with no such impact observed in the absence of light exposure. Interestingly, riboflavin exhibited no/minimal growth-inhibitory effects on the normal cell line L929, irrespective of light conditions. Moreover, through EtBr displacement (DNA-EtBr) and the TUNEL assay, it has been illustrated that, upon exposure to visible light, riboflavin can intercalate within DNA and induce DNA damage. In conclusion, under visible light conditions, riboflavin emerges as a promising candidate with a selective and effective potent anticancer agent against breast cancer while exerting a minimal influence on regular cellular activity.

乳腺癌的靶向治疗需要尽量减少对健康细胞的伤害。我们的研究探索了核黄素作为一种可通过光照射激活的靶向抗癌化合物的潜力。在这里,我们将与时间相关的密度泛函理论(TD-DFT)计算与可见光下的体外研究相结合。TD-DFT 计算显示,电子电荷从 DNA 碱基转移到核黄素,最显著的激发峰出现在可见光范围内。在这些见解的指导下,我们对乳腺癌细胞株 MCF-7 和 MDA-MB-231 进行了体外研究。结果表明,在可见光下暴露于核黄素时,这些细胞株的生长受到了极大的抑制,而在没有光照射的情况下则没有观察到这种影响。有趣的是,无论光照条件如何,核黄素对正常细胞株 L929 的生长抑制作用都很小。此外,通过 EtBr 置换(DNA-EtBr)和 TUNEL 检测,可以说明在可见光照射下,核黄素可以在 DNA 内插层并诱导 DNA 损伤。总之,在可见光条件下,核黄素是一种很有前途的候选物质,对乳腺癌具有选择性和有效的强效抗癌作用,同时对正常细胞活动的影响很小。
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引用次数: 0
CageCavityCalc (C3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages CageCavityCalc (C3):计算和观察分子笼空腔的计算工具
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2024-07-09 DOI: 10.1021/acs.jcim.4c00355
Vicente Martí-Centelles, Tomasz K. Piskorz, Fernanda Duarte
Organic(porous) and metal–organic cages are promising biomimetic platforms with diverse applications spanning recognition, sensing, and catalysis. The key to the emergence of these functions is the presence of well-defined inner cavities capable of binding a wide range of guest molecules and modulating their properties. However, despite the myriad cage architectures currently available, the rational design of structurally diverse and functional cages with specific host–guest properties remains challenging. Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce CageCavityCalc (C3), a Python-based tool for calculating the cavity size of molecular cages. The code is available on GitHub at https://github.com/VicenteMartiCentelles/CageCavityCalc. C3 utilizes a novel algorithm that enables the rapid calculation of cavity sizes for a wide range of molecular structures and porous systems. Moreover, C3 facilitates easy visualization of the computed cavity size alongside hydrophobic and electrostatic potentials, providing insights into host–guest interactions within the cage. Furthermore, the calculated cavity can be visualized using widely available visualization software, such as PyMol, VMD, or ChimeraX. To enhance user accessibility, a PyMol plugin has been created, allowing nonspecialists to use this tool without requiring computer programming expertise. We anticipate that the deployment of this computational tool will significantly streamline cage cavity calculations, thereby accelerating the discovery of functional cages.
有机(多孔)笼和金属有机笼是前景广阔的仿生平台,具有识别、传感和催化等多种应用。实现这些功能的关键在于存在能够结合多种客体分子并调节其性质的定义明确的内腔。然而,尽管目前有无数的笼子结构,但合理设计具有特定主客体特性的结构多样的功能性笼子仍然具有挑战性。有效预测这些特性对于加速新型功能笼的发现至关重要。在此,我们介绍基于 Python 的计算分子笼空腔尺寸的工具 CageCavityCalc (C3)。代码可在 GitHub 上获取:https://github.com/VicenteMartiCentelles/CageCavityCalc。C3 采用了一种新颖的算法,可以快速计算各种分子结构和多孔系统的空腔尺寸。此外,C3 还能方便地将计算出的空腔尺寸与疏水和静电势一起可视化,从而深入了解笼子内的主客体相互作用。此外,计算出的空腔还可通过广泛使用的可视化软件(如 PyMol、VMD 或 ChimeraX)进行可视化。为了提高用户的使用便利性,我们还创建了一个 PyMol 插件,让非专业人员也能使用这一工具,而无需计算机编程专业知识。我们预计,这一计算工具的部署将大大简化笼腔计算,从而加速功能笼的发现。
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引用次数: 0
期刊
Journal of Chemical Information and Modeling
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