Trends in application of advancing computational approaches in GPCR ligand discovery

Abstract

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Why Search for Alternative GPCR Agonists?

“…Furthermore, these rules-apparently against the wishes of the initiator [51]-were frequently invoked to described the projects from hit to drug. The design of new entities complying with this rule from the structure of natural agonists as leader compounds or ex nihilo by structure-based drug design [52] approaches must then be considered. For example, the druggability of GHSR1, physiologically activated by ghrelin, a 23-amino-acid O-octanoylated peptide [53], has led to the synthesis of numerous compounds including JMV 1843 [54].…”

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

“…1−6 The experimental structures of GPCRs in combination with the application of molecular dynamics (MD) simulations 1−6 and enhanced sampling methods 4,7 enable protein flexibility and help toward understanding their conformational heterogeneity 8 and ligand binding. 4,7 Examples of SBDD and LBDD and application of MD simulations to understand ligand binding against GPCRs are cited in ref 4. Experimental structures have been resolved for three out of the four subtypes of human adenosine receptors (hARs). 9 These include the structures of the active and inactive states of the human A 2A receptor (hA 2A R), 10−20 the human A 1 receptor (hA 1 R), 21−23 and the human A 2B receptor (hA 2B R).…”

“…Structure-based drug design (SBDD) against G protein-coupled receptors (GPCRs) has benefited from the rapid accumulation of experimentally resolved structures of ligand–GPCR complexes. Moreover, ligand-based drug design (LBDD) has been developed with the evolution of database and artificial intelligence technology. − The experimental structures of GPCRs in combination with the application of molecular dynamics (MD) simulations − and enhanced sampling methods , enable protein flexibility and help toward understanding their conformational heterogeneity and ligand binding. , Examples of SBDD and LBDD and application of MD simulations to understand ligand binding against GPCRs are cited in ref .…”

“…Moreover, ligand-based drug design (LBDD) has been developed with the evolution of database and artificial intelligence technology. 1−6 The experimental structures of GPCRs in combination with the application of molecular dynamics (MD) simulations 1−6 and enhanced sampling methods 4,7 enable protein flexibility and help toward understanding their conformational heterogeneity 8 and ligand binding. 4,7 Examples of SBDD and LBDD and application of MD simulations to understand ligand binding against GPCRs are cited in ref 4. Experimental structures have been resolved for three out of the four subtypes of human adenosine receptors (hARs).…”

pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures

“…Many modifications for structural studies are usually needed ( Milić and Veprintsev, 2015 ) and in the last years, new approaches to facilitate GPCR structure elucidation were assessed including recombinant overexpression and purification strategies ( Errey and Fiez-Vandal, 2020 ), crystallization platforms ( Parker and Newstead, 2012 ) and detergent studies ( Lee et al , 2020 ). These experimental efforts coupled with the evolution of computational methods [molecular dynamics, integrative modelling and machine learning ( Zhu et al , 2021 )] led to the development of high-quality models for 3D structures of GPCR deposited in dedicated repositories, such as the GPCRdb ( Pándy-Szekeres et al , 2018 ) and GPCR-EXP. Despite structural data availability, the absence of 3D structures for many GPCRs has limited the ability to employ rational structure-based drug development approaches ( Heifetz et al , 2015 ).…”