Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor

Abstract

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.

Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

“…Both antagonist-GPR18 complexes were stable during the 200 ns MD simulation runs, which supports our prediction of the binding pocket based on docking studies. The duration of the MD simulation runs was in accordance with similar studies performed for other GPCRs [62][63][64][65]. The behavior of antagonists 4 and 5 in the homology model of GPR18 during the 200 ns MD simulation is presented from a bird's eye view perspective in Supplementary Information Figures S2 and S3.…”

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15

“…We provide evidence that GPR15 acts as an immunomodulator and can be considered as a novel target for immunotherapy for the four cancers. We also predicted the 3D structure of human GPR15 and applied structure-based virtual screening (SBVS) approaches [29][30][31][32][33][34][35][36][37] to discover potential antagonists that bind to the predicted active site. These results help us to understand the role of GPR15 in carcinogenesis and its future prospective for STAD drug development.…”

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15

“…We provide evidences that GPR15 acts as an immunomodulator and can be considered as a novel target for immunotherapy for four cancers. We also predicted the 3D structure of human GPR15 and apply structure-based virtual screening (SBVS) approaches [29][30][31][32][33]to discover potential antagonists that bind to the predicted active site. These results could help us understand the role of GPR15 in carcinogenesis and its future prospective for STAD drug development.…”