Demystifying the Molecular Basis of Pyrazoloquinolinones Recognition at the Extracellular α1+/β3- Interface of the GABAA Receptor by Molecular Modeling

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

The Binding of Alpinia galanga Oil and Its Nanoemulsion to Mammal GABAA Receptors Using Rat Cortical Membranes and an In Silico Modeling Platform

The anesthetic effect of Alpinia galanga oil (AGO) has been reported. However, knowledge of its pathway in mammals is limited. In the present study, the binding of AGO and its key compounds, methyl eugenol, 1,8-cineole, and 4-allylphenyl acetate, to gamma-aminobutyric acid type A (GABA_A) receptors in rat cortical membranes, was investigated using a [³H]muscimol binding assay and an in silico modeling platform. The results showed that only AGO and methyl eugenol displayed a positive modulation at the highest concentrations, whereas 1,8-cineole and 4-allylphenyl acetate were inactive. The result of AGO correlated well to the amount of methyl eugenol in AGO. Computational docking and dynamics simulations into the GABA_A receptor complex model (PDB: 6X3T) showed the stable structure of the GABA_A receptor–methyl eugenol complex with the lowest binding energy of −22.16 kcal/mol. This result shows that the anesthetic activity of AGO and methyl eugenol in mammals is associated with GABA_A receptor modulation. An oil-in-water nanoemulsion containing 20% w/w AGO (NE-AGO) was formulated. NE-AGO showed a significant increase in specific [³H]muscimol binding, to 179% of the control, with an EC₅₀ of 391 µg/mL. Intracellular studies show that normal human cells are highly tolerant to AGO and the nanoemulsion, indicating that NE-AGO may be useful for human anesthesia.

Structure-Guided Computational Methods Predict Multiple Distinct Binding Modes for Pyrazoloquinolinones in GABAA Receptors

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.