Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Abstract

SummaryAccurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

Integration of Ligand‐ and Target‐Based Virtual Screening for the Discovery of Cruzain Inhibitors

“…However, it has been extended to combine ligand-and target-based virtual screening methods. [49][50] Since the programs used in this work produce their rank lists with different metrics (i.e. energy, pK i or similarity index), the reported values were scaled or scored according to the respective ranking method, to become metric-independent, and submitted to three distinct consensus scoring methods: scaled-rank-bynumber, rank-by-rank and rank-by-vote.…”

Medicinal Chemistry of the A3 Adenosine Receptor: Agonists, Antagonists, and Receptor Engineering

“…9) deserves to be mentioned here, not only because of its good binding profile as an A 3 AR antagonist, but also (especially) due to the novelty of the strategy applied to its design, which was based on a 3D database-searching approach (Novellino et al 2005). There is increasing evidence of the importance of 2D/3D database searching as a valuable tool to discover novel lead compounds for the A 3 AR and for other G-protein-coupled receptors (GPCRs) (Costanzi et al 2008). …”

Stabilised G protein-coupled receptors in structure-based drug design: a case study with adenosine A2A receptor

“…22 These recent advances in generating high-quality GPCR structural information have sparked much interest in the field and have greatly enhanced our understanding of receptor activation and function. 16,[23][24][25][26][27][28][29][30] Furthermore, this structural insight may now be used to drive the rational design of drug-like molecules with high degrees of potency and selectivity for their intended GPCR target by expediting the analysis of shape-and electrostatic-complementarity between the ligands and their binding sites. 31,32 Such structure-based drug design (SBDD) approaches have been highly successful with soluble targets such as kinases and proteases, 33 …”