Article Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand–Receptor Interactions Wallace K.B. Chan 1,2 and Yang Zhang 1,3 1 - Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA 2 - Department of Pharmacology, University of Michigan, Ann Arbor, MI 48109, USA 3 - Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Correspondence to Yang Zhang: Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA.
[email protected] https://doi.org/10.1016/j.jmb.2020.07.003 Edited by Michael Sternberg Abstract G protein-coupled receptors (GPCRs) are a large family of integral membrane proteins responsible for cellular signal transductions. Identification of therapeutic compounds to regulate physiological processes is an important first step of drug discovery. We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein structure prediction and structure-based binding-site identification, followed by homologous GPCR detections through structure and orthosteric binding-site comparisons. Ligand profiles constructed from the homologous ligand–GPCR complexes are then used to thread through compound databases for VS. The pipeline was first tested in a large-scale retrospective screening experiment against 224 human Class A GPCRs, where MAGELLAN achieved a median enrichment factor (EF) of 14.38, significantly higher than that using individual ligand profiles. Next, MAGELLAN was examined on 5 and 20 GPCRs from two public VS databases (DUD-E and GPCR-Bench) and resulted in an average EF of 9.75 and 13.70, respectively, which compare favorably with other state-of- the-art docking- and ligand-based methods, including AutoDock Vina (with EF = 1.48/3.16 in DUD-E and GPCR-Bench), DOCK 6 (2.12/3.47 in DUD-E and GPCR-Bench), PoLi (2.2 in DUD-E), and FINDSITEC- comb2.0 (2.90 in DUD-E).