G protein-coupled receptors (GPCR) comprise the largest family of membrane proteins and are of considerable interest as targets for drug development. However, many GPCR structures remain unsolved. To address the structural ambiguity of these receptors, computational tools such as homology modeling and loop modeling are often employed to generate predictive receptor structures. Here we combined both methods to benchmark a protocol incorporating homology modeling based on a locally selected template and extracellular loop modeling that additionally evaluates the presence of template ligands during these modeling steps. Ligands were also docked using three docking methods and two pose selection methods to elucidate an optimal ligand pose selection method. Results suggest that local template-based homology models followed by loop modeling produce more accurate and predictive receptor models than models produced without loop modeling, with decreases in average receptor and ligand RMSD of 0.54 Å and 2.91 Å, respectively. Ligand docking results showcased the ability of MOE induced fit docking to produce ligand poses with atom root-mean-square deviation (RMSD) values at least 0.20 Å lower (on average) than the other two methods benchmarked in this study. In addition, pose selection methods (software-based scoring, ligand complementation) selected lower RMSD poses with MOE induced fit docking than either of the other methods (averaging at least 1.57 Å lower), indicating that MOE induced fit docking is most suited for docking into GPCR homology models in our hands. In addition, target receptor models produced with a template ligand present throughout the modeling process most often produced target ligand poses with RMSD values ≤ 4.5 Å and Tanimoto coefficients > 0.6 after selection based on ligand complementation than target receptor models produced in the absence of template ligands. Overall, the findings produced by this study support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions.

G 蛋白偶联受体 (GPCR) 包括最大的膜蛋白家族，作为药物开发的靶点具有相当大的兴趣。然而，许多 GPCR 结构仍未解决。为了解决这些受体的结构歧义，通常采用同源建模和环建模等计算工具来生成预测性受体结构。在这里，我们将这两种方法结合起来，以基于本地选择的模板和细胞外环建模来对包含同源建模的协议进行基准测试，该协议还评估了这些建模步骤中模板配体的存在。还使用三种对接方法和两种姿势选择方法对接配体，以阐明最佳配体姿势选择方法。结果表明，与没有环建模的模型相比，基于局部模板的同源模型和环建模产生更准确和更具预测性的受体模型，平均受体和配体 RMSD 分别降低 0.54 Å 和 2.91 Å。配体对接结果展示了 MOE 诱导拟合对接产生配体姿态的能力，原子均方根偏差 (RMSD) 值比本研究中的其他两种方法低（平均）至少 0.20 Å。此外，姿势选择方法（基于软件的评分、配体互补）选择了更低的 RMSD 姿势，MOE 诱导拟合对接比其他任何一种方法（平均至少低 1.57 Å），表明 MOE 诱导拟合对接最适合对接进入我们手中的 GPCR 同源模型。此外，在整个建模过程中使用模板配体生成的目标受体模型与在没有模板配体的情况下生成的目标受体模型相比，基于配体互补进行选择后，最常生成的目标配体的 RMSD 值≤ 4.5 Å 且 Tanimoto 系数 > 0.6。总体而言，本研究得出的结果支持在模板晶体结构中存在配体的情况下，将局部模板同源性建模与 de novo ECL2 建模结合使用，以生成旨在研究配体结合相互作用的 GPCR 模型。6 基于配体互补选择后的靶受体模型比在没有模板配体的情况下产生的。总体而言，本研究得出的结果支持在模板晶体结构中存在配体的情况下，将局部模板同源性建模与 de novo ECL2 建模结合使用，以生成旨在研究配体结合相互作用的 GPCR 模型。6 基于配体互补选择后的靶受体模型比在没有模板配体的情况下产生的。总体而言，本研究得出的结果支持在模板晶体结构中存在配体的情况下，将局部模板同源性建模与 de novo ECL2 建模结合使用，以生成旨在研究配体结合相互作用的 GPCR 模型。