MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland.
InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland.
Molecules. 2023 Apr 13;28(8):3420. doi: 10.3390/molecules28083420.
Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.
分子对接是虚拟筛选(VS)中用于识别药物发现靶标小分子配体的关键方法。虽然对接提供了一种理解和预测蛋白质-配体复合物形成的切实方法,但在实际 VS 应用中,对接算法往往无法将活性配体与非活性分子区分开来。在这里,我们展示了一种新的对接和形状聚焦药效团 VS 协议,以使用维甲酸受体相关孤儿受体γ t(RORγt)作为案例研究来促进有效的命中发现。RORγt 是治疗炎症性疾病(如牛皮癣和多发性硬化症)的有前途的靶标。首先,灵活地对接商业分子数据库。其次,根据反映靶结合腔的负像(NIB)模型的形状/静电势对替代对接构象进行重新评分。通过使用贪婪搜索驱动的算法或蛮力 NIB 优化对 NIB 模型进行迭代修剪和基准测试来优化 NIB 模型的组成。第三,进行基于药效团点的过滤,将命中识别集中在已知的 RORγt 活性热点上。第四,对剩余分子进行自由能结合亲和力评估。最后,选择了 28 种化合物进行体外测试,其中 8 种化合物被确定为低 μM 范围的 RORγt 抑制剂,这表明引入的 VS 协议产生了约 29%的有效命中率。