用于癌症治疗的潜在小分子TIPE2抑制剂的计算机研究

Decis. Sci. Pub Date : 2023-10-07 DOI:10.3390/sci5040039
Jerica Wilson, Katerina Evangelou, Youhai H. Chen, Hai-Feng Ji
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引用次数: 0

摘要

背景:自19世纪以来,慢性炎症就与癌症有关。肿瘤生长是由慢性炎症所需的促血管生成因子支持的。极化的白细胞启动这些血管生成和肿瘤发生的因素。TIPE2是一种转运蛋白,负责细胞骨架重排,使极化的白细胞具有运动性。抑制这种蛋白可能会导致实体肿瘤癌症的治疗选择;然而,由于TIPE2蛋白的空腔较大,迄今为止还没有开发出这样的抑制剂。在这里,我们通过结合基于结构和基于片段的药物设计方法研究了可能的小分子抑制剂。最高结合配体与蛋白质络合,片段库与配合物对接,目的是连接被击中的化合物和片段,以设计更有效的配体。通过硅基结构筛选鉴定了三个命中化合物,并通过将片段连接到命中化合物上鉴定了一个具有良好结合亲和力的连接化合物C2-F14。C2-F14在分子动力学模拟中表现出良好的结合稳定性和良好的ADME预测性能。方法:采用AutoDock Vina 1.1.2软件进行高通量分子对接计算。使用AutoDock Vina与PyRx进行单个配体的分子对接。配体文库采用OpenBabel法制备,连接配体采用阿伏伽德罗法制备。使用AutoDockTools-1.5.6制备蛋白。用PyMOL可视化蛋白质配体复合物。用BIOVIA Discovery Studio Visualizer绘制了蛋白质与配体相互作用的二维和三维表示。使用SwissADME计算硅的吸收、分布、代谢和排泄(ADME)特性。用GROMACS进行分子动力学模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In Silico Study of Potential Small Molecule TIPE2 Inhibitors for the Treatment of Cancer
Context: Chronic inflammation has been linked to cancer since the 19th century. Tumor growth is supported by the proangiogenic factors that chronic inflammation requires. Polarized leukocytes initiate these angiogenic and tumorigenic factors. TIPE2, a transport protein, manages the cytoskeletal rearrangement that gives a polarized leukocyte its motility. Inhibition of this protein could lead to a therapeutic option for solid tumor cancers; however, no such inhibitors have been developed so far due to the large cavity size of the TIPE2 protein. Here we have examined possible small molecule inhibitors by combining structure-based and fragment-based drug design approaches. The highest binding ligands were complexed with the protein, and fragment libraries were docked with the complex with the intention of linking the hit compounds and fragments to design a more potent ligand. Three hit compounds were identified by in silico structure-based screening and a linked compound, C2–F14, of excellent binding affinity, was identified by linking fragments to the hit compounds. C2–F14 demonstrates good binding stability in molecular dynamic simulations and great predicted ADME properties. Methods: High throughput molecular docking calculations of mass libraries were performed using AutoDock Vina 1.1.2. Molecular docking of individual ligands was performed using AutoDock Vina with PyRx. Ligand libraries were prepared using OpenBabel, linked ligands were prepared using Avogadro. The protein was prepared using AutoDockTools-1.5.6. Protein-ligand complexes were visualized with PyMOL. Two- and three-dimensional representations of protein–ligand interactions were plotted with BIOVIA Discovery Studio Visualizer. In silico absorption, distribution, metabolism, and excretion (ADME) properties were calculated using SwissADME. Molecular dynamics simulations were conducted with GROMACS.
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