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
{"title":"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","authors":"Ying Zhang, Guifu Xu, Puhua Wu","doi":"10.1007/s10822-026-00820-7","DOIUrl":null,"url":null,"abstract":"<div><p>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 <b>50</b>. 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 (q<sup>2</sup> = 0.792, r<sup>2</sup> = 0.999, <span>\\( r_{{pred}}^{2} \\)</span> = 0.998, <span>\\( r_{{m(over{\\text{ }}all)}}^{2} \\)</span> = 0.978) and CoMSIA model (q<sup>2</sup> = 0.713, r<sup>2</sup> = 0.996, <span>\\( r_{{pred}}^{2} \\)</span> = 0.978, <span>\\( r_{{m(over{\\text{ }}all)}}^{2} \\)</span> = 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, <span>\\( r_{{pred}}^{2} \\)</span> = 0.862; CoMSIA-SEHDA, <span>\\( r_{{pred}}^{2} \\)</span> = 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.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"40 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer-Aided Molecular Design","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s10822-026-00820-7","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
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.
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.