Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, DHHS, Bethesda, MD 20892, USA.
J Mol Graph Model. 2011 Feb;29(5):614-23. doi: 10.1016/j.jmgm.2010.11.005. Epub 2010 Nov 19.
G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs.
G 蛋白偶联受体（GPCRs）调节广泛的生理功能，具有很大的药物学兴趣。本文以β（2）-肾上腺素受体为例，探讨基于对接的虚拟筛选在发现 GPCR 配体中的适用性，并定义了旨在提高筛选性能的方法。我们的受控计算实验是在包含已知激动剂和拮抗剂以及大量诱饵的化合物数据集上进行的。基于与反向激动剂 carazolol 结合的受体的结构进行筛选，产生了出色的结果，对配体和诱饵进行了明确的优先级排序。阻滞剂通常优于激动剂；然而，激动剂也与诱饵有明显的区别。设计了一种方法来通过生成受体的替代构象的集合来增加筛选产量，该集合考虑了受体的灵活性。此外，还设计了一种方法来基于已知激动剂优化受体，以提高激动剂的检索效果。最后，证明了基于对接的虚拟筛选对具有不同精度水平的同源模型也具有适用性。这最后一点非常重要，因为仅为有限数量的 GPCR 提供了晶体结构，并且将我们的结论扩展到整个超家族。这项分析的结果肯定支持将计算机辅助技术应用于发现新型 GPCR 配体，尤其是考虑到在不久的将来，预计将解决实验结构并使其可供越来越多的 GPCR 使用。
