Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

Abstract

The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.

Exhaustively Exploring the Prevalent Interaction Pathways of Ligands Targeting the Ligand-Binding Pocket of Farnesoid X Receptor via Combined Enhanced Sampling

“…In this study, 11 FXR ligands, including nine agonists (or partial agonists) and two antagonists, were considered for the investigation (Table ). Since the remarkable difference of the active and inactive conformations of an NR protein (especially the Helix-12 region), the conformational selection model is employed as the basis to analyze the dissociation pathway of the investigated ligands, that is, an agonist prefers to bind with the active conformation of a NR protein, while an antagonist favors in binding with the inactive conformation of the protein, , and they bind to the corresponding conformation of a NR protein directly without causing large conformational change of the protein. Twenty repeats of RAMD simulation were conducted for each system to explore the potential dissociation pathways of the FXR ligands.…”

Discovery of Potent and Selective CB2 Agonists Utilizing a Function-Based Computational Screening Protocol

“…We found that the antigenic peptides have similar immunological responses as long as their LRIPs are similar. In 2022, Wang et al also found that residue–ligand interaction pattern could reflect not only the binding affinity but also the function of a ligand . Inspired by those successful studies, in this paper, we proposed a novel computational approach that allows us to rationally screen and design agonists that target specific receptors.…”

“…In 2022, Wang et al also found that residue−ligand interaction pattern could reflect not only the binding affinity but also the function of a ligand. 47 Inspired by those successful studies, in this paper, we proposed a novel computational approach that allows us to rationally screen and design agonists that target specific receptors. Considering that CB2 is a promising drug target, which has many indications, and the crystal or cryo-EM structures of active/inactive CB1/CB2 are available, CB1/CB2 is an ideal model system to validate our computational protocol.…”