病毒核定位信号肽和抑制剂配体与导入素-α核转运蛋白的竞争性结合

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-06-13 DOI:10.1021/acs.jcim.4c00626
Bryan M. Delfing, Xavier E. Laracuente, William Jeffries, Xingyu Luo, Audrey Olson, Kenneth W. Foreman, Greg Petruncio, Kyung Hyeon Lee, Mikell Paige, Kylene Kehn-Hall, Christopher Lockhart and Dmitri K. Klimov*, 
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引用次数: 0

摘要

委内瑞拉马脑炎病毒(VEEV)是一种高致病性病原体,其外壳蛋白中的核定位信号(NLS)序列能与宿主的importin-α转运蛋白结合并阻止核导入。我们研究了两种小配体(称为 I1 和 I2)干扰 VEEV 的 NLS 肽与导入素-α 蛋白结合的分子机制。为此,我们进行了全原子复制交换分子动力学模拟,探究了 VEEV 核心 NLS 肽和 I1 或 I2 配体与导入蛋白-α 主要 NLS 结合位点的竞争性结合。作为参考,我们使用了之前的模拟,研究了 coreNLS 肽或抑制剂与导入蛋白-α 的非竞争性结合。我们发现,这两种抑制剂都能完全消除 coreNLS 肽的原生结合,迫使它在导入蛋白-α 主要 NLS 结合位点内采取多种非原生的松散结合姿势。这两种抑制剂主要是通过遮蔽其氨基酸来破坏原生 coreNLS 结合的稳定性,而不是与之竞争,使其与导入蛋白-α 结合。与 I1 不同的是,I2 是在主要 NLS 结合位点的边缘异位结合,因此它抑制的核心 NLS 本源结合相互作用比 I1 少。在有或没有抑制剂竞争的情况下,通过计算核心 NLS 肽与导入蛋白-α结合的自由能,可以支持结构分析。具体来说,两种抑制剂都降低了 coreNLS 结合的自由能增益,其中 I1 造成的损失明显大于 I2。为了检验我们的模拟结果,我们进行了 AlphaScreen 实验,测量两种抑制剂的 IC50 值。结果发现,I1 的 IC50 值低于 I2。我们推测 I1 和 I2 配体的抑制作用可能是针对 VEEV 的帽状蛋白中的 NLS 的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Competitive Binding of Viral Nuclear Localization Signal Peptide and Inhibitor Ligands to Importin-α Nuclear Transport Protein

Venezuelan equine encephalitis virus (VEEV) is a highly virulent pathogen whose nuclear localization signal (NLS) sequence from capsid protein binds to the host importin-α transport protein and blocks nuclear import. We studied the molecular mechanisms by which two small ligands, termed I1 and I2, interfere with the binding of VEEV’s NLS peptide to importin-α protein. To this end, we performed all-atom replica exchange molecular dynamics simulations probing the competitive binding of the VEEV coreNLS peptide and I1 or I2 ligand to the importin-α major NLS binding site. As a reference, we used our previous simulations, which examined noncompetitive binding of the coreNLS peptide or the inhibitors to importin-α. We found that both inhibitors completely abrogate the native binding of the coreNLS peptide, forcing it to adopt a manifold of nonnative loosely bound poses within the importin-α major NLS binding site. Both inhibitors primarily destabilize the native coreNLS binding by masking its amino acids rather than competing with it for binding to importin-α. Because I2, in contrast to I1, binds off-site localizing on the edge of the major NLS binding site, it inhibits fewer coreNLS native binding interactions than I1. Structural analysis is supported by computations of the free energies of the coreNLS peptide binding to importin-α with or without competition from the inhibitors. Specifically, both inhibitors reduce the free energy gain from coreNLS binding, with I1 causing significantly larger loss than I2. To test our simulations, we performed AlphaScreen experiments measuring IC50 values for both inhibitors. Consistent with in silico results, the IC50 value for I1 was found to be lower than that for I2. We hypothesize that the inhibitory action of I1 and I2 ligands might be specific to the NLS from VEEV’s capsid protein.

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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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