Thomas Brittany N, Parrill Abby L, Baker Daniel L
The University of Memphis, Department of Chemistry and Computational Research on Materials Institute (CROMIUM), USA.
The University of Memphis, Department of Chemistry and Computational Research on Materials Institute (CROMIUM), USA.
J Mol Graph Model. 2022 May;112:108119. doi: 10.1016/j.jmgm.2021.108119. Epub 2021 Dec 28.
G protein-coupled receptors (GPCR) are the largest family of cell surface receptors in vertebrates. Their abundance and role in nearly all physiological systems make GPCR the largest protein family targeted for development of pharmaceuticals. Ligand discovery aimed at identification of chemical tools and drug leads is aided by molecular docking simulations that allow critical analysis of the potential interactions between small molecules and proteins in resulting complexes. However, blind assessments of ligand pose quality and affinity prediction have thus far not provided broadly generalizable performance expectations for docking into experimentally-characterized GPCR targets. Likewise, the relative importance of receptor activation state and ligand function differences have also not been systematically assessed. This study compares performance when docking ligands of varied function into varied GPCR activation states in the absence of extensive resampling of the input GPCR structure, and only limited sidechain flexibility after ligand placement. Simulations were performed using 37 experimental structures of 11 Class A GPCR crystallized in multiple activation states (giving rise to 37 self-docking and 68 cross docking simulations). Our results show that one specific subset of cross-docking simulations gave results of similar quality to self-docking. Median ligand RMSD values for top-scored poses were 1.2 Å and 2.0 Å for self-docking and State/Function cross-docking, respectively. The distributions of ligand RMSD values were not statistically different for these two conditions, according to a Kolmogorov-Smirnov test. Therefore, docking performance against GPCR targets can be estimated in advance based on docking target structure activation states, with higher accuracy expected when docking agonists into active state structures and inverse agonists or antagonists into inactive state structures. Receptor conformational sampling in advance of docking or receptor conformational adjustment after docking are more likely to produce substantial improvements for other pairings of receptor activation state and ligand function.
G蛋白偶联受体（GPCR）是脊椎动物中最大的细胞表面受体家族。它们在几乎所有生理系统中的丰富性和作用，使GPCR成为药物开发的最大蛋白质靶点家族。旨在识别化学工具和药物先导物的配体发现，借助分子对接模拟得以推进，这种模拟能够对小分子与所得复合物中蛋白质之间的潜在相互作用进行关键分析。然而，迄今为止，对配体构象质量的盲目评估和亲和力预测，尚未为对接实验表征的GPCR靶点提供广泛适用的性能预期。同样，受体激活状态和配体功能差异的相对重要性也尚未得到系统评估。本研究比较了在不对输入的GPCR结构进行广泛重采样且配体放置后仅有限侧链灵活性的情况下，将不同功能的配体对接至不同GPCR激活状态时的性能。使用11种A类GPCR的37个实验结构进行模拟，这些结构在多种激活状态下结晶（产生37次自对接和68次交叉对接模拟）。我们的结果表明，交叉对接模拟的一个特定子集给出了与自对接质量相似的结果。自对接和状态/功能交叉对接中，得分最高构象的配体RMSD值中位数分别为1.2 Å和2.0 Å。根据Kolmogorov-Smirnov检验，这两种情况下配体RMSD值的分布没有统计学差异。因此，基于对接靶点结构的激活状态，可以预先估计针对GPCR靶点的对接性能，将激动剂对接至活性状态结构以及将反向激动剂或拮抗剂对接至非活性状态结构时，预期准确性更高。对接前的受体构象采样或对接后的受体构象调整，更有可能为受体激活状态和配体功能的其他配对带来实质性改善。
