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, <InlineEquation ID="IEq181"> <EquationSource Format="TEX">\( r_{{pred}}^{2} \)</EquationSource> </InlineEquation> = 0.998, <InlineEquation ID="IEq1810"> <EquationSource Format="TEX">\( r_{{m(over{\text{ }}all)}}^{2} \)</EquationSource> </InlineEquation> = 0.978) and CoMSIA model (q2 = 0.713, r2 = 0.996, <InlineEquation ID="IEq183"> <EquationSource Format="TEX">\( r_{{pred}}^{2} \)</EquationSource> </InlineEquation> = 0.978, <InlineEquation ID="IEq18101"> <EquationSource Format="TEX">\( r_{{m(over{\text{ }}all)}}^{2} \)</EquationSource> </InlineEquation> = 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, <InlineEquation ID="IEq1833"> <EquationSource Format="TEX">\( r_{{pred}}^{2} \)</EquationSource> </InlineEquation> = 0.862; CoMSIA-SEHDA, <InlineEquation ID="IEq1831"> <EquationSource Format="TEX">\( r_{{pred}}^{2} \)</EquationSource> </InlineEquation> = 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

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

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发表日期：

2026年5月4日

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Ying Zhang,
Guifu Xu,
Puhua Wu

摘要

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