Dual - Effect Dopamine Receptor Inhibitor Design And Multi - Target Recognition Method Validation

Psychotic disease severely destroys human’s health. In the past decades, the pathology of schizophrenia and action mechanism of antipsychotics have been intensively studied. Many targets of antipsychotics were uncovered and dopamine receptor was regarded as one of the most important targets. It was widely reported that D1 agonist/D2 antagonist or D2/D3 antagonist can be the potential second-generation antipsychotics, i.e., atypical antipsychotics. Firstly, they inhibit the dopamine D2 receptor, which is the typical characteristic of psychotics. Secondly, they activate the dopamine D1 receptor in the prefrontal cortext. Thirdly, psychotics can improve the negative symptoms and cognitive functions of schizophrenia patients by inhibiting the dopamine D3. Therefore, discovery of drugs targeted at dopamine D1, D2 and D3 receptors is of great importance in the treatment of schizophrenia. In this thesis, we found 14 active compounds with high affinities targeted at dopamine receptors by using the technologies of computer-aided drug design and virtual screening. The improved "dynamic pharmacophore model" strategy is used to discover the active compounds targeted at dopamine D1, D2 and D3 receptors. Furthermore, the interaction mechanism of the active ingredient of a natural product,l-stepholidine (l-SPD) with the dopamine D1, D2, D3 receptors was investigated by integrating molecular dynamics simulation and docking strategies. In the last section, a target database containing a highly qualified dataset that is composed of 1714 entries from 1594 known drug targets covering 18 biochemical functions was constructed and five inverse docking schemes were evaluated in multiple targets identification.Dopamine receptors belong to the protein family of G-protein coupled receptor. Since the crystal structures of dopamine receptors have not been characterized, their models are firstly constructed based on the high-resolution X-ray structure ofβ2-adrenergic receptor by using homology modeling method. Then, molecular dynamics simulation was conducted on the systems of proteins inserted in hydrated lipid bilayer to obtain the dynamical protein conformations. Furthermore, the active sites of dopamine receptors were mapped by grid calculation and several target-based pharmacophore models were built. After validating the accuracy of the pharmacophore models, virtual screening was carried out on two available compound database:Spece and Maybridge. Finally,95 candidates were purchased and 14 of them were proved to show high activity against dopamine D1, D2 or D3 receptors.Molecular dynamics simulation on macromolecular systems is helpful in eliciting structural and dynamical features of proteins, protein-ligand interaction mode, protein-protein interaction mode, etc. In this thesis, we carried out the molecular dynamics simulations on l-SPD-D1,l-SPD-D2 and l-SPD-D3 complexes to investigate the binding modes of l-SPD with D1, D2 and D3, the agonistic mechanism of D1, antagonistic mechanism of D2 and D3 caused by l-SPD. The results indicated that the interaction of l-SPDs with D1, D2 and D3 retained the conserved binding mode:the conserved Asp3.32 formed salt-bridge with the protonated nitrogen of l-SPD, the Ser5.42 and Ser5.46 formed stable hydrogen bond with the oxygen of ring-D in l-SPD-D1 and l-SPD-D2, but the difference came from the interaction of rings A and B of l-SPD with dopamine receptors. Hydrogen bond networks between transmembrane helices of receptors played important roles in the structural stability. The dynamic behaviors of Trp6.48 and D/ERY motif on the third transmembrane helix were different among l-SPD-D1,l-SPD-D2 and l-SPD-D3. The Trp6.48, D/ERY and the kink structure of sixth transmembrane helix are relevant to the activation of D1 receptor.The lack of accurate and efficient methods for target identification has been the bottleneck in drug discovery. In recent years, inverse docking has been applied as an efficient method in target identification. In this thesis, a target database containing a highly qualified dataset that is composed of 1714 entries from 1594 known drug targets was collected as a testing pool. Five inverse docking engines including GOLD, FlexX, Tarfisdock and two in-house target search schemes TarSearch-X and TarSearch-M were evaluated by eight multiple target systems in the dataset. The results show that TarSearch-X is the most effective method in multiple targets identification and validation, and the effectiveness of GOLD in multiple targets identification is also acceptable. This target database and assessment of inverse docking strategies will be very helpful in predicting the potential targets and undesirable effects of drugs.