Automated, Score‐Based Pharmacophore Generation Using Multiple Copy Simultaneous Search

Abstract

Pharmacophores are three‐dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental screening. G protein‐coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Pharmacophore models are most typically ligand‐based and constructed via the identification of structural commonalities between known bioactive ligands. However, structure‐based pharmacophore models (requiring only a target protein’s structure) provide an alternative to ligand‐based pharmacophore models. Although both ligand‐based and structure‐based pharmacophore models have exhibited success in prior virtual screening studies, most pharmacophore modeling efforts are not applicable to GPCR targets lacking known ligands. Consequently, the development of a purely structure‐based pharmacophore modeling protocol for GPCR has become an increasingly attractive approach as the number of publicly available, high‐resolution GPCR structures has increased (113 unique GPCR represented as of November 24, 2021). Thus, the aim of this work was to develop a structure‐based pharmacophore modeling approach that can generate well‐performing pharmacophore models in GPCR crystal structures and homology models. Pharmacophore models were generated in crystal structures and homology models of 13 class A GPCR via automated feature annotation of differing subsets of functional group fragments placed with Multiple Copy Simultaneous Search (MCSS). During the feature annotation process, distance cutoffs were enforced to generate combinations of pharmacophore features that reflect spatial arrangements of interactions typically observed in GPCR pharmacophore models. Resulting pharmacophore models for each target GPCR were used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor (EF) and goodness‐of‐hit (GH) metrics to assess performance. To identify well‐performing pharmacophore models for cases where active ligands are unknown, pharmacophore models were classified using cluster‐then‐predict logistic regression. As a proof‐of principle for this method, pharmacophore models were generated for the orphan GPCR GPR101 to elucidate candidate ligands that may possess activity for GPR101. Application of this method to the set of 13 class A GPCR targets resulted in the generation of pharmacophore models possessing EF scores ≥ 2 in both crystal structures (12 of 13 cases) and homology models (9 of 13 cases). In addition, classification of pharmacophore models with cluster‐then‐predict logistic regression resulted in positive predictive values (PPV) of at least 0.67 for pharmacophore models generated with selected MCSS fragment subsets. Lastly, implementation of generated pharmacophore models in a virtual screening workflow identified 33 candidate ligands for GPR101.