MRGPRX2 ligandome: Molecular simulations reveal three categories of ligand-receptor interactions

• Insights into ligand-receptor interactions for mast cell activation via Mas-related G protein-coupled receptor (MRGPR) X2. • GLU164, ASP184, PHE170, TRP243, PHE244 and PHE257 are key to bind small molecule ligands. • MRGPRX2 ligands show common features: protonated amine moieties and large hydrophobic domains. • Molecular recognition involves induced-fit via salt-bridge / π-cation and hydrophobic / π-stacking interactions. Mas-related G protein-coupled receptor (MRGPR) X2 is a mast cell receptor known to be activated by a wide range of ligands of various size, charge and origin. Our aim is to gain a deeper understanding of the binding processes of the different MRGPRX2 ligands and the ligand-receptor interactions in order to identify crucial structural elements for receptor activation. We used the three-dimensional structure of MRGPRX2 described in Nature in 2021 by Cao et al. and Yang et al. to computationally model the interaction between MRGPRX2 and small molecule ligands under simulated physiological conditions. Docking and post-docking samplings of the MRGPRX2 ligandome within the GPCR binding pocket led to the identification of key structural features for protein–ligand interactions. On the ligand side, we obtained an overlay of different molecular patterns or chemical groups by comparing different ligands plotted on the receptor. These key features include at least one protonated amine moiety of MRGPRX2 ligands contributing to one salt-bridge and one π-cation interaction, as well as an extended non-polar domain of the ligand surface that offer hydrophobic segregation and/or π-stacking interactions. In the receptor, we identified amino acids (GLU164, ASP184, PHE101, PHE170, TRP243, PHE244 and PHE257) that specifically interact via hydrogen bonding, salt-bridges, π-cation interactions and π-π stacking with the ligands to direct binding and ultimately receptor activation. Our insights into ligand-receptor interaction obtained by molecular modeling can help to predict mast cell activation via MRGPRX2 including adverse reactions, and facilitate the development of MRGPRX2 antagonists for the treatment of mast cell-mediated diseases.