Frontiers in Neuroscience (Jan 2021)
Structure-Guided Computational Methods Predict Multiple Distinct Binding Modes for Pyrazoloquinolinones in GABAA Receptors
Abstract
Pyrazoloquinolinones (PQs) are a versatile class of GABAA receptor ligands. It has been demonstrated that high functional selectivity for certain receptor subtypes can be obtained by specific substitution patterns, but so far, no clear SAR rules emerge from the studies. As is the case for many GABAA receptor targeting chemotypes, PQs can interact with distinct binding sites on a given receptor pentamer. In pentamers of αβγ composition, such as the most abundant α1β2γ2 subtype, many PQs are high affinity binders of the benzodiazepine binding site at the extracellular α+/γ2− interfaces. There they display a functionally near silent, flumazenil-like allosteric activity. More recently, interactions with extracellular α+/β− interfaces have been investigated, where strong positive modulation can be steered toward interesting subtype preferences. The most prominent examples are functionally α6-selective PQs. Similar to benzodiazepines, PQs also seem to interact with sites in the transmembrane domain, mainly the sites used by etomidate and barbiturates. This promiscuity leads to potential contributions from multiple sites to net modulation. Developing ligands that interact exclusively with the extracellular α+/β− interfaces would be desired. Correlating functional profiles with binding sites usage is hampered by scarce and heterogeneous experimental data, as shown in our meta-analysis of aggregated published data. In the absence of experimental structures, bound states can be predicted with pharmacophore matching methods and with computational docking. We thus performed pharmacophore matching studies for the unwanted sites, and computational docking for the extracellular α1,6+/β3− interfaces. The results suggest that PQs interact with their binding sites with diverse binding modes. As such, rational design of improved ligands needs to take a complex structure-activity landscape with branches between sub-series of derivatives into account. We present a workflow, which is suitable to identify and explore potential branching points on the structure-activity landscape of any small molecule chemotype.
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