The dopamine D2 receptor (D2R) is one of the principal therapeutic targets for the treatment of schizophrenia and other neuropsychiatric disorders. Understanding how ligands with different pharmacological profiles interact with D2R is essential for the rational design of safer and more effective antipsychotic drugs. In this work, Molecular Dynamics (MD) simulations combined with Quantum Theory of Atoms in Molecules (QTAIM) analysis were employed to investigate the electronic nature of protein–ligand interactions in D2R embedded in a neuronal membrane environment. Representative agonists (dopamine and rotigotine) and antipsychotics from different generations (haloperidol, risperidone, and aripiprazole) were analyzed to identify interaction patterns associated with distinct pharmacological activities. The agonist-bound simulations revealed recurrent interactions involving the serine-rich region, whereas the antipsychotic-bound systems exhibited more persistent contacts within the central aromatic region of the binding pocket. These observations suggest ligand-associated interaction tendencies rather than universal determinants of agonism or antagonism. Furthermore, aripiprazole displayed a unique interaction profile characterized by enhanced coupling with the PIF connector, suggesting a distinct modulation of the TM6 toggle switch compared with other antipsychotics. The integration of MD and electron density topology revealed ligand-specific interaction networks associated with distinct pharmacological profiles at D2R. The interaction patterns identified in this study highlight characteristic interaction motifs associated with ligand-specific pharmacological profiles and provide mechanistic insights that may support the rational design of novel dopaminergic therapeutics.
Gerardo Padilla-Bernal, L. D. Herrera-Zúñiga, Rubicelia Vargas· International Journal of Mol...· 0 citations
In 2025, we released UAM-Ixachi to democratize, simplify, and accelerate molecular docking and virtual screening methods. It is a free, open-source, and user-friendly tool. Building on that work, we present SMASH, which features several upgrades: it predicts binding sites using machine learning, automatically determines titration states, utilizes a Graphics Process Unit for molecular docking, combines machine learning with energy scoring functions, and clusters data using the K-means algorithm. Tests demonstrated that the tool might handle large projects within a reasonable time using local computational resources. In automatic mode, the tool can accurately reproduce a high percentage of ligand poses from Protein Data Bank complexes via docking simulation. It also differentiated between the predictions of active and decoy ligands from a DUD-E data set in a reasonable amount of time. SMASH is freely available at https://smashreleases.z13.web.core.windows.net/ We implemented several software solutions to prepare and execute molecular docking simulations: PDB2PQR, P2Rank, MGL Tools, OpenBabel, AutoDock-GPU, Vina-GPU, and SCORCH. The MMFF94, AD4, and Vina force fields are part of the implemented tools.
A. Suárez-Alonso, L. D. Herrera-Zúñiga, Mayra Lozano-Espinosa et al.· Journal of Molecular Modelin...· 0 citations