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通过虚拟筛选鉴定多巴胺 D3 受体配体,并探索命中化合物的结合模式。

Identifying Dopamine D3 Receptor Ligands through Virtual Screening and Exploring the Binding Modes of Hit Compounds.

机构信息

Key Laboratory of Structure-Based Drug Design and Discovery Ministry of Education, Department of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.

Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China.

出版信息

Molecules. 2023 Jan 5;28(2):527. doi: 10.3390/molecules28020527.

Abstract

The dopamine D3 receptor (D3R) is an important central nervous system target for treating various neurological diseases. D3R antagonists modulate the improvement of psychostimulant addiction and relapse, while D3R agonists can enhance the response to dopaminergic stimulation and have potential applications in treating Parkinson’s disease, which highlights the importance of identifying novel D3R ligands. Therefore, we performed auto dock Vina-based virtual screening and D3R-binding-affinity assays to identify human D3R ligands with diverse structures. All molecules in the ChemDiv library (>1,500,000) were narrowed down to a final set of 37 molecules for the binding assays. Twenty-seven compounds exhibited over 50% inhibition of D3R at a concentration of 10 μM, and 23 compounds exhibited over 70% D3R inhibition at a concentration of 10 μM. Thirteen compounds exhibited over 80% inhibition of D3R at a concentration of 10 μM and the IC50 values were measured. The IC50 values of the five compounds with the highest D3R-inhibition rates ranged from 0.97 μM to 1.49 μM. These hit compounds exhibited good structural diversity, which prompted us to investigate their D3R-binding modes. After trial and error, we combined unbiased molecular dynamics simulation (MD) and molecular mechanics generalized Born surface area (MM/GBSA) binding free-energy calculations with the reported protein−ligand-binding pose prediction method using induced-fit docking (IFD) and binding pose metadynamics (BPMD) simulations into a self-consistent and computationally efficient method for predicting and verifying the binding poses of the hit ligands to D3R. Using this IFD-BPMD-MD-MM/GBSA method, we obtained more accurate and reliable D3R−ligand-binding poses than were obtained using the reported IFD-BPMD method. This IFD-BPMD-MD-MM/GBSA method provides a novel paradigm and reference for predicting and validating other protein−ligand binding poses.

摘要

多巴胺 D3 受体 (D3R) 是治疗各种神经疾病的重要中枢神经系统靶点。D3R 拮抗剂可调节改善精神兴奋剂成瘾和复发,而 D3R 激动剂可以增强对多巴胺刺激的反应,并有潜力应用于治疗帕金森病,这凸显了鉴定新型 D3R 配体的重要性。因此,我们进行了基于 AutoDock Vina 的虚拟筛选和 D3R 结合亲和力测定,以鉴定具有不同结构的人类 D3R 配体。ChemDiv 文库中的所有分子(>1,500,000 个)被缩小到用于结合测定的最终 37 个分子。27 种化合物在 10 μM 浓度下对 D3R 的抑制率超过 50%,23 种化合物在 10 μM 浓度下对 D3R 的抑制率超过 70%。13 种化合物在 10 μM 浓度下对 D3R 的抑制率超过 80%,并测量了 IC50 值。五个抑制率最高的化合物的 IC50 值范围为 0.97 μM 至 1.49 μM。这些命中化合物表现出良好的结构多样性,促使我们研究它们的 D3R 结合模式。经过反复试验,我们将无偏分子动力学模拟 (MD) 和分子力学广义 Born 表面积 (MM/GBSA) 结合自由能计算与报告的蛋白-配体结合构象预测方法相结合,使用诱导拟合对接 (IFD) 和结合构象元动力学 (BPMD) 模拟,形成一种自洽且计算高效的方法,用于预测和验证命中配体与 D3R 的结合构象。使用这种 IFD-BPMD-MD-MM/GBSA 方法,我们获得了比使用报告的 IFD-BPMD 方法更准确和可靠的 D3R-配体结合构象。这种 IFD-BPMD-MD-MM/GBSA 方法为预测和验证其他蛋白-配体结合构象提供了一种新的范例和参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/519f/9862751/875e003be7f5/molecules-28-00527-g001.jpg

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