Fragment-Based Computational Method for Designing GPCR Ligands

• Published in: Journal of chemical information and modeling 2020-09, Vol.60 (9), p.4339-4349
• Main Authors: Li, Yan, Sun, Yaping, Song, Yunpeng, Dai, Dongcheng, Zhao, Zhixiong, Zhang, Qing, Zhong, Wenge, Hu, Liaoyuan A, Ma, Yingli, Li, Xun, Wang, Renxiao
• Format: Article
• Language: English
• Subjects: Binding; Chemical bonds; Coordination compounds; Crystallography; Design; Fragments; Free energy; Hydrogen bonds; Ligands; Molecular dynamics; Receptors
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.9b00699

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, which is arguably the most important family of drug target. With the technology breakthroughs in X-ray crystallography and cryo-electron microscopy, more than 300 GPCR-ligand complex structures have been publicly reported since 2007, covering about 60 unique GPCRs. Such abundant structural information certainly will facilitate the structure-based drug design by targeting GPCRs. In this study, we have developed a fragment-based computational method for designing novel GPCR ligands. We first extracted the characteristic interaction patterns (CIPs) on the binding interfaces between GPCRs and their ligands. The CIPs were used as queries to search the chemical fragments derived from GPCR ligands, which were required to form similar interaction patterns with GPCR. Then, the selected chemical fragments were assembled into complete molecules by using the AutoT&T2 software. In this work, we chose β-adrenergic receptor (β-AR) and muscarinic acetylcholine receptor (mAChR) as the targets to validate this method. Based on the designs suggested by our method, samples of 63 compounds were purchased and tested in a cell-based functional assay. A total of 15 and 22 compounds were identified as active antagonists for β2-AR and mAChR M1, respectively. Molecular dynamics simulations and binding free energy analysis were performed to explore the key interactions (e.g., hydrogen bonds and π–π interactions) between those active compounds and their target GPCRs. In summary, our work presents a useful approach to the de novo design of GPCR ligands based on the relevant 3D structural information.