Insights into the binding mechanism and structural requirements of LPAR2 antagonists as antifibrotic agents based on homology modeling, molecular docking, prediction of membrane permeability and 3D QSAR

IF 3.1 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ying Zhang, Guifu Xu, Puhua Wu
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引用次数: 0

Abstract

Lysophosphatidic acid receptor 2 (LPAR2), a G protein-coupled receptor, has been implicated in the progression of fibrosis and is therefore a promising novel drug target for the treatment of fibrosis and related diseases. In this paper, a reliable homology model of LPAR2 was obtained by using three templates (PDB IDs: 4Z34, 7TD0, and 7VIE) and evaluations. A new binding site for a series of selective LPAR2 inhibitors were identified through molecular docking with the reference compound 50. Subsequently, a three-dimensional quantitative structure-activity relationship (3D QSAR) analysis was conducted on a series of N-sulfonyl heterocyclic antagonists of LPAR2. The derived optimal CoMFA model (q2 = 0.792, r2 = 0.999, \( r_{{pred}}^{2} \) = 0.998, \( r_{{m(over{\text{ }}all)}}^{2} \)  = 0.978) and CoMSIA model (q2 = 0.713, r2 = 0.996, \( r_{{pred}}^{2} \) = 0.978, \( r_{{m(over{\text{ }}all)}}^{2} \) = 0.958) demonstrated strong statistical robustness and high external predictability. The 3D contour maps generated from these models were analyzed and compared with the binding mode of the reference compound. This provided insights into the structural requirements of these LPAR2-selective inhibitors. Furthermore, the predictive capability of these models was validated by accurately predicting the antagonistic activities of other types of LPAR2-selective inhibitors (CoMFA-SE, \( r_{{pred}}^{2} \) = 0.862; CoMSIA-SEHDA, \( r_{{pred}}^{2} \)  = 0.934), confirming the robustness of the optimal 3D QSAR models. The new binding site and the optimal 3D QSAR models will be helpful to design novel molecules and predict their inhibitory activity against LPAR2.

Graphical abstract

The alternative text for this image may have been generated using AI.

Abstract Image

基于同源建模、分子对接、膜通透性预测和3D QSAR研究LPAR2拮抗剂作为抗纤维化药物的结合机制和结构要求
溶血磷脂酸受体2 (LPAR2)是一种G蛋白偶联受体,与纤维化的进展有关,因此是治疗纤维化和相关疾病的有希望的新药物靶点。本文利用3个模板(PDB id: 4Z34、7TD0和7VIE)和评价得到了可靠的LPAR2同源性模型。通过与参考化合物50的分子对接,确定了一系列选择性LPAR2抑制剂的新结合位点。随后,对一系列n -磺酰基杂环LPAR2拮抗剂进行了三维定量构效关系(3D QSAR)分析。导出的最优CoMFA模型(q2 = 0.792, r2 = 0.999,[公式:见文]= 0.998,[公式:见文]= 0.978)和CoMSIA模型(q2 = 0.713, r2 = 0.996,[公式:见文]= 0.978,[公式:见文]= 0.958)具有较强的统计稳稳性和较高的外部可预测性。对这些模型生成的三维等高线图进行了分析,并与参考化合物的结合方式进行了比较。这为这些lpar2选择性抑制剂的结构要求提供了见解。此外,通过准确预测其他类型lpar2选择性抑制剂的拮抗活性(CoMFA-SE,[公式:见文]= 0.862;CoMSIA-SEHDA,[公式:见文]= 0.934),验证了这些模型的预测能力,证实了最优3D QSAR模型的稳健性。新的结合位点和最佳的3D QSAR模型将有助于设计新的分子并预测其对LPAR2的抑制活性。
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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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