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
Reliable density functional theory (DFT) methods for thermally driven cyclization reactions remain insufficiently established, particularly for systems in which proton transfer, conformational preorganization, and weak environmental effects jointly shape the energy landscape. Here, we present a systematic benchmark of DFT exchange–correlation functional/basis-set combinations against CCSD(T) reference energies for the thermal Conia–Ene cyclization, using chain length and explicit water assistance as chemically relevant probes of functional performance. Representative reaction profiles were constructed for two model substrates (hex-5-ynal and hept-6-ynal), considering pathways with and without explicit water, in both gas phase and implicit solvent. The reaction proceeds stepwise through enolization followed by cyclization, with enolization defining the intrinsic kinetic bottleneck in the water-free pathway (ca. 70 kcal mol–1). Explicit water selectively lowers this barrier by up to 30 kcal mol–1 through proton-transfer mediation, whereas the cyclization barrier is primarily controlled by chain length through transition-state preorganization; implicit solvation has only a minor energetic effect. Across 38 approximate exchange–correlation functionals and 14 basis sets, range-separated hybrids provide the most accurate and balanced description of both activation barriers and reaction energies. In particular, ωB97XD and CAM-B3LYP-D3BJ achieve near-chemical accuracy, with mean absolute errors close to 1 kcal mol–1 when combined with triple-ζ basis sets containing polarization and diffuse functions. Comparable accuracy is also achieved by the double-hybrid functionals mPW2PLYP and DSD-PBEP86, albeit at a substantially higher computational cost. Overall, this study establishes a practical CCSD(T)-referenced benchmark and identifies robust DFT protocols for modeling thermal Conia–Ene cyclizations, with expected transferability to more complex substrates and confined environments.
J. Gutiérrez-Flores, Eduardo H. Huerta, Javier Serrano Medina et al.· ACS Omega· 0 citations
