One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data

Abstract

G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can be stimulated by agonists, or inhibited by antagonists or inverse agonists. The development of a new drug targeting a member of this family requires to take into account the pharmacological profile of the designed ligands in order to elicit the desired response. The structure-based virtual screening of chemical libraries may prioritize a specific class of ligands by combining docking results and ligand binding information provided by crystallographic structures. The performance of the method depends on the relevance of the structural data, in particular the conformation of the targeted site, the binding mode of the reference ligand, and the approach used to compare the interactions formed by the docked ligand with those formed by the reference ligand in the crystallographic structure. Here, we propose a new method based on the conformational dynamics of a single protein–ligand reference complex to improve the biased selection of ligands with specific pharmacological properties in a structure-based virtual screening exercise. Interactions patterns between a reference agonist and the receptor, here exemplified on the β2 adrenergic receptor, were extracted from molecular dynamics simulations of the agonist/receptor complex and encoded in graphs used to train a one-class machine learning classifier. Different conditions were tested: low to high affinity agonists, varying simulation duration, considering or ignoring hydrophobic contacts, and tuning of the classifier parametrization. The best models applied to post-process raw data from retrospective virtual screening obtained by docking of test libraries effectively filtered out irrelevant poses, discarding inactive and non-agonist ligands while identifying agonists. Taken together, our results suggest that consistency of the binding mode during the simulation is a key to the success of the method.

Predicting the duration of action of β2‐adrenergic receptor agonists: Ligand and structure‐based approaches

“…The original training workflow of IG‐OCSVM [15] was updated to include the current work's results (Figure 7). Originally the average MD binding mode was compared to the reference crystallographic structure to determine the quality of the simulation data, to include targets without a resolved experimental structure we propose to use any available experimental data, such as mutagenesis experiment, to evaluate the results.…”

“…The most used technique is protein‐ligand docking, which predicts the pose of a molecule in the target's binding site and evaluates its binding affinity using a scoring function. Pharmacophore models, based on the interactions observed in crystallographic structures [14] or molecular dynamic simulations [15], were often used to post‐process docking results, especially to identify ligands with a desired pharmacological profile. SBDD strategies were applied using both crystallographic structures of the receptor‐agonist complex [16, 17], and structures generated by an in silico pipeline combining AlphFold2 (AF) [18] and molecular docking.…”

“…This analysis was performed considering different descriptors, different machine learning methods, as well as various degrees of information (one reference, two references with different duration, a curated dataset). We also explored the use of interaction‐based methods in SBDD for such task, since they were already successfully applied for the detection of ligands with a specific pharmacological profile, even with limited available experimental data [15]. The presence of a bias towards ligands with a specific duration of action in SBDD interaction‐based methods could be linked to the used representation indirectly encoding some ligand properties.…”

“…Protein-ligand docking, and molecular dynamics were used to model the complex between ADRB2 and the agonists formoterol and salbutamol. The modelled and crystallographic binding modes were used for pose rescoring in a retrospective docking exercise using the dynamic representation obtained combining interaction graphs (IGs) and one class support vector machine (IG-OCSVM) [15].…”