Potential inhibitors of extra-synaptic NMDAR/TRPM4 interaction: Screening, molecular docking, and structure-activity analysis

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-08-01 DOI:10.1016/j.comtox.2023.100279
Elif Deniz , Fuat Karakuş , Burak Kuzu
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引用次数: 0

Abstract

Over-activation of extra-synaptic NMDARs by excessive glutamate is known to cause excitotoxicity. The molecular mechanism of how this excitotoxicity occurs was revealed recently. This paper presents the results of in silico studies aimed at finding potential small-molecule inhibitors that can block this mechanism, namely the extra-synaptic NMDAR/TRPM4 interaction. We screened for small molecules according to 2D (at least Tanimoto threshold was 90%) and/or 3D similarity, molecular weight, lipophilicity using control compounds (C8 and C19) targeting this interaction. We then pre-filtered these molecules according to their drug-likeness and toxicity profiles. After pre-filtering, we performed a docking study against the extra-synaptic NMDAR/TRPM4 interaction with the remaining 26 compounds. In addition, we determined that selected compounds exhibit low affinity for classical NMDAR ligand binding sites. Ultimately, we identified four novel compounds (C8-12, C8-15, C19-3, C19-4) that could block the extra-synaptic NMDAR/TRPM4 interaction without inhibiting the normal function of synaptic NMDARs.

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突触外NMDAR/TRPM4相互作用的潜在抑制剂:筛选、分子对接和结构活性分析
过量谷氨酸导致突触外NMDARs的过度激活可引起兴奋性毒性。这种兴奋性毒性发生的分子机制最近才被揭示出来。本文介绍了旨在寻找潜在的小分子抑制剂的计算机研究结果,这些小分子抑制剂可以阻断这种机制,即突触外NMDAR/TRPM4相互作用。我们根据2D(至少谷本阈值为90%)和/或3D相似性、分子量、亲脂性筛选小分子,使用对照化合物(C8和C19)靶向这种相互作用。然后,我们根据它们的药物相似性和毒性特征对这些分子进行预过滤。预过滤后,我们对其余26个化合物的突触外NMDAR/TRPM4相互作用进行对接研究。此外,我们确定所选化合物对经典NMDAR配体结合位点具有低亲和力。最终,我们发现了四种新的化合物(C8-12, C8-15, C19-3, C19-4),它们可以阻断突触外NMDAR/TRPM4的相互作用,而不会抑制突触NMDAR的正常功能。
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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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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