Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation

药效团 虚拟筛选 G蛋白偶联受体 计算生物学 药物发现 计算机科学 化学 立体化学 生物 受体 生物化学
作者
Gregory L. Szwabowski,Jon C. Cole,Daniel L. Baker,Abby L. Parrill
出处
期刊:Journal of Molecular Graphics & Modelling [Elsevier]
卷期号:121: 108429-108429 被引量:1
标识
DOI:10.1016/j.jmgm.2023.108429
摘要

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 testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.
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