Structures of the human dopamine D3 receptor-Gi complexes.

The dopamine system, including five dopamine receptors (D1R-D5R), plays essential roles in the central nervous system (CNS), and ligands that activate dopamine receptors have been used to treat many neuropsychiatric disorders. Here, we report two cryo-EM structures of human D3R in complex with an inhibitory G protein and bound to the D3R-selective agonists PD128907 and pramipexole, the latter of which is used to treat patients with Parkinson's disease. The structures reveal agonist binding modes distinct from the antagonist-bound D3R structure and conformational signatures for ligand-induced receptor activation. Mutagenesis and homology modeling illuminate determinants of ligand specificity across dopamine receptors and the mechanisms for Gi protein coupling. Collectively our work reveals the basis of agonist binding and ligand-induced receptor activation and provides structural templates for designing specific ligands to treat CNS diseases targeting the dopaminergic system.

Multitargeting approaches to cognitive impairment: Synthesis of aryl-alkylpiperazines and assessment at cholinesterases, histamine H3 and dopamine D3 receptors.

Multitargeting ligands on enzymes and receptors may generate a profile for a potential treatment of cognitive impairment. Considering this, a set of 21 substituted aryl-alkyl-piperazines were designed, prepared and tested for their binding affinities at histamine H3 and dopamine D3 receptors (H3R and D3R, respectively) as well as acetyl- and butyrylcholinesterases (AChE/BChE) as potentially synergistic profile. Initial screening of the compounds at H3R and D3R was done at 1 or 10 µM and 100 µM at AChE and BChE assays. The most promising compounds were then evaluated in full concentration-response curves to estimate the Ki and IC50 values. Results showed that several compounds were ligands at H3R (n = 10), D3R (n = 6), AChE (n = 3), and BChE (n = 9). Compounds LINS05006 (Ki H3R 2.8 µM; D3R 0.7 µM; IC50 BChE 26.3 µM) and LINS05015 (Ki H3R 1.1 µM; D3R 3.1 µM; IC50 AChE 97.8 µM; BChE 43.7 µM) are highlighted since presented affinity in three different. These results suggest that methylpiperazine moiety led to balanced activity at all three classes of targets, and longer linker provided the best affinities. These compounds presented high ligand efficiency values (LE > 0.3) and may have adequate pharmacokinetic profile as suggested by calculated physicochemical properties.

Impact of the Substitution Pattern at the Basic Center and Geometry of the Amine Fragment on 5-HT6 and D3R Affinity in the 1H-Pyrrolo[3,2-c]quinoline Series.

Salt bridge (SB, double-charge-assisted hydrogen bonds) formation is one of the strongest molecular non-covalent interactions in biological systems, including ligand-receptor complexes. In the case of G-protein-coupled receptors, such an interaction is formed by the conserved aspartic acid (D3.32) residue and the basic moiety of the aminergic ligand. This study aims to determine the influence of the substitution pattern at the basic nitrogen atom and the geometry of the amine moiety at position 4 of 1H-pyrrolo[3,2-c]quinoline on the quality of the salt bridge formed in the 5-HT6 receptor and D3 receptor. To reach this goal, we synthetized and biologically evaluated a new series of 1H-pyrrolo[3,2-c]quinoline derivatives modified with various amines. The selected compounds displayed a significantly higher 5-HT6R affinity and more potent 5-HT6R antagonist properties when compared with the previously identified compound PZ-1643, a dual-acting 5-HT6R/D3R antagonist; nevertheless, the proposed modifications did not improve the activity at D3R. As demonstrated by the in silico experiments, including molecular dynamics simulations, the applied structural modifications were highly beneficial for the formation and quality of the SB formation at the 5-HT6R binding site; however, they are unfavorable for such interactions at D3R.

Dopamine Receptor D1R and D3R and GRK4 Interaction in Hypertension.

Essential hypertension is caused by the interaction of genetic, behavioral, and environmental factors. Abnormalities in the regulation of renal ion transport cause essential hypertension. The renal dopaminergic system, which inhibits sodium transport in all the nephron segments, is responsible for at least 50% of renal sodium excretion under conditions of moderate sodium excess. Dopaminergic signals are transduced by two families of receptors that belong to the G protein-coupled receptor (GPCR) superfamily. D1-like receptors (D1R and D5R) stimulate, while D2-like receptors (D2R, D3R, and D4R) inhibit adenylyl cyclases. The dopamine receptor subtypes, themselves, or by their interactions, regulate renal sodium transport and blood pressure. We review the role of the D1R and D3R and their interaction in the natriuresis associated with volume expansion. The D1R- and D3R-mediated inhibition of renal sodium transport involves PKA and PKC-dependent and -independent mechanisms. The D3R also increases the degradation of NHE3 via USP-mediated ubiquitinylation. Although deletion of Drd1 and Drd3 in mice causes hypertension, DRD1 polymorphisms are not always associated with human essential hypertension and polymorphisms in DRD3 are not associated with human essential hypertension. The impaired D1R and D3R function in hypertension is related to their hyper-phosphorylation; GRK4γ isoforms, R65L, A142V, and A486V, hyper-phosphorylate and desensitize D1R and D3R. The GRK4 locus is linked to and GRK4 variants are associated with high blood pressure in humans. Thus, GRK4, by itself, and by regulating genes related to the control of blood pressure may explain the "apparent" polygenic nature of essential hypertension.

Dopamine D3 receptor in the nucleus accumbens alleviates neuroinflammation in a mouse model of depressive-like behavior.

We recently reported that dopamine D3 receptor (D3R) was involved in inflammation-related depression. Nucleus accumbens (NAc) inflammation is implicated in the development and progression of depression, but its regulatory mechanism remains largely unknown. In a mouse model of NAc neuroinflammation induced by bilateral NAc injection of lipopolysaccharide (LPS), we observed that NAc neuroinflammation triggered depressive-like behaviors, and D3R expression decline and microglial activation in the NAc. A selective knockdown of D3R in the NAc elicited depressive-like behaviors, while re-expression of D3R in the NAc of global D3RKO mice alleviated depressive-like behaviors induced by D3R deficiency. D3R downregulation in the NAc shifted microglia toward a proinflammatory state, which was validated with cultured mouse microglial cultures. Further in vitro results demonstrated that D3R inhibition induced microglia to enter a proinflammatory state primarily through the Akt signaling pathway. In conclusion, our results suggest that D3R expression in the NAc may inhibit microglial proinflammatory responses in the NAc, thus alleviating NAc neuroinflammation and subsequent depressive-like behaviors through the Akt signaling pathway.

BOPPY-based novel fluorescent dopamine D2 and D3 receptor ligands.

Dopamine is one of the crucial neurotransmitters in the human brain. Its out-of-range concentration can lead to various neurological diseases with special interest for dopamine D2 and D3 receptor subtypes. Although BODIPY is a highly versatile structural moiety for fluorescence labeling, we have looked out for structurally related pyridine-based moieties. We used BOPPY labelling of well-described D2R/D3R pharmacophores to obtain ligands with moderate to low nanomolar binding affinities as well as low to excellent quantum yields for bright fluorescence ligands. To best of our knowledge, this is the first report on the application of BOPPY fluorophores to GPCR ligands. This approach offers a general applicable way for fluorescence labelling via primary aliphatic amine elements.

Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.

Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein.

Development of an Automatic Pipeline for Participation in the CELPP Challenge.

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining-whenever possible-empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein-ligand complexes, which will be addressed in future versions of the pipeline.

Simultaneous Antagonism at H3R/D2R/D3R Reduces Autism-like Self-Grooming and Aggressive Behaviors by Mitigating MAPK Activation in Mice.

Dysregulation in brain neurotransmitters underlies several neuropsychiatric disorders, e.g., autism spectrum disorder (ASD). Also, abnormalities in the extracellular-signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) pathway pave the way for neuroinflammation, neurodegeneration, and altered learning phenotype in ASD. Therefore, the effects of chronic systemic administration of the multiple-targeting antagonist ST-713 at the histamine H3 receptor (H3R) and dopamine D2/D3 receptors (D2/D3R) on repetitive self-grooming, aggressive behaviors, and abnormalities in the MAPK pathway in BTBR T + Itpr3tf/J (BTBR) mice were assessed. The results showed that ST-713 (2.5, 5, and 10 mg/kg, i.p.) mitigated repetitive self-grooming and aggression in BTBR mice (all p < 0.05), and the ameliorative effects of the most promising dose of ST-713 (5 mg/kg, i.p.) on behaviors were completely abrogated by co-administration of the H3R agonist (R)-α-methylhistamine or the anticholinergic drug scopolamine. Moreover, the elevated levels of several MAPK pathway proteins and induced proinflammatory markers such as tumor necrosis factor (TNF-α), interleukin-1ß (IL-1ß), and IL-6 were significantly suppressed following chronic administration of ST-713 (5 mg/kg, i.p.) (all p < 0.01). Furthermore, ST-713 significantly increased the levels of histamine and dopamine in hippocampal tissue of treated BTBR mice (all p < 0.01). The current observations signify the potential role of such multiple-targeting compounds, e.g., ST-713, in multifactorial neurodevelopmental disorders such as ASD.

New tetrahydroisoquinoline-based D3R ligands with an o-xylenyl linker motif.

The effect of rigidification of the n-butyl linker region of tetrahydroisoquinoline-containing D3R ligands via inclusion of an o-xylenyl motif was examined in this study. Generally, rigidification with an o-xylenyl linker group reduces D3R affinity and negatively impacts selectivity versus D2R for compounds possessing a 6-methoxy-1,2,3,4,-tetrahydroisoquinolin-7-ol primary pharmacophore group. However, D3R affinity appears to be regulated by the primary pharmacophore group and high affinity D3R ligands with 6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline and 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline primary pharmacophore groups were identified. The results of this study also indicate that D3R selectivity versus the σ2R is dictated by the benzamide secondary pharmacophore group, this being facilitated with 4-substituted benzamides. Compounds 5s and 5t were identified as high affinity (Ki < 4 nM) D3R ligands. Docking studies revealed that the added phenyl ring moiety interacts with the Cys181 in D3R which partially accounts for the strong D3R affinity of the ligands.

The Multi-Targeting Ligand ST-2223 with Histamine H3 Receptor and Dopamine D2/D3 Receptor Antagonist Properties Mitigates Autism-Like Repetitive Behaviors and Brain Oxidative Stress in Mice.

Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder characterized by social and communicative impairments, as well as repetitive and restricted behaviors (RRBs). With the limited effectiveness of current pharmacotherapies in treating repetitive behaviors, the present study determined the effects of acute systemic treatment of the novel multi-targeting ligand ST-2223, with incorporated histamine H3 receptor (H3R) and dopamine D2/D3 receptor affinity properties, on ASD-related RRBs in a male Black and Tan BRachyury (BTBR) mouse model of ASD. ST-2223 (2.5, 5, and 10 mg/kg, i.p.) significantly mitigated the increase in marble burying and self-grooming, and improved reduced spontaneous alternation in BTBR mice (all p < 0.05). Similarly, reference drugs memantine (MEM, 5 mg/kg, i.p.) and aripiprazole (ARP, 1 mg/kg, i.p.), reversed abnormally high levels of several RRBs in BTBR (p < 0.05). Moreover, ST-2223 palliated the disturbed anxiety levels observed in an open field test (all p < 0.05), but did not restore the hyperactivity parameters, whereas MEM failed to restore mouse anxiety and hyperactivity. In addition, ST-2223 (5 mg/kg, i.p.) mitigated oxidative stress status by decreasing the elevated levels of malondialdehyde (MDA), and increasing the levels of decreased glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT) in different brain parts of treated BTBR mice (all p < 0.05). These preliminary in vivo findings demonstrate the ameliorative effects of ST-2223 on RRBs in a mouse model of ASD, suggesting its pharmacological prospective to rescue core ASD-related behaviors. Further confirmatory investigations on its effects on various brain neurotransmitters, e.g., dopamine and histamine, in different brain regions are still warranted to corroborate and expand these initial data.

Microglial activation contributes to depressive-like behavior in dopamine D3 receptor knockout mice.

We previously demonstrated that the dopamine D3 receptor (D3R) inhibitor, NGB2904, increases susceptibility to depressive-like symptoms, elevates pro-inflammatory cytokine expression, and alters brain-derived neurotrophic factor (BDNF) levels in mesolimbic dopaminergic regions, including the medial prefrontal cortex (mPFC), nucleus accumbens (NAc), and ventral tegmental area (VTA) in mice. The mechanisms by which D3R inhibition affects neuroinflammation and onset of depression remain unclear. Here, using D3R-knockout (D3RKO) and congenic wild-type C56BL/6 (WT) mice, we demonstrated that D3RKO mice displayed depressive-like behaviors, increased tumornecrosisfactor-α (TNF-α), interleukin-1ß (IL-1ß), and IL-6 levels, and altered BDNF expression in selected mesolimbic dopaminergic regions. D3R expression was localized to astrocytes or microglia in the mPFC, NAc, and VTA in WT mice. D3RKO mice exhibited a large number of Iba1-labelled microglia in the absence of glial fibrillary acidic protein (GFAP)-labelled astrocytes in mesolimbic dopaminergic brain areas. Inhibition or ablation of microglia by minocycline (25 mg/kg and 50 mg/kg) or PLX3397 (40 mg/kg) treatment ameliorated depressive-like symptoms, alterations in pro-inflammatory cytokine levels, and BDNF expression in the indicated brain regions in D3RKO mice. Minocycline therapy alleviated the increase in synaptic density in the NAc in D3RKO mice. These findings suggest that microglial activation in selected mesolimbic reward regions affects depressive-like behaviors induced by D3R deficiency.

Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.

We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.

Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structure-based, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the L2 regularization hyperparameter, alpha. We also developed a novel metric of ligand three-dimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates that detailed parameterization, careful data training and implementation, and extensive feature analysis are necessary to obtain strong performance with more complex machine learning methods. Post hoc analysis shows that scoring functions based only on ligand features are competitive with those also using structural features. Our DNN approach tied for fifth in predicting BACE-ligand binding affinities.

The role of human in the loop: lessons from D3R challenge 4.

The rapid development of new machine learning techniques led to significant progress in the area of computer-aided drug design. However, despite the enormous predictive power of new methods, they lack explainability and are often used as black boxes. The most important decisions in drug discovery are still made by human experts who rely on intuitions and simplified representation of the field. We used D3R Grand Challenge 4 to model contributions of human experts during the prediction of the structure of protein-ligand complexes, and prediction of binding affinities for series of ligands in the context of absence or abundance of experimental data. We demonstrated that human decisions have a series of biases: a tendency to focus on easily identifiable protein-ligand interactions such as hydrogen bonds, and neglect for a more distributed and complex electrostatic interactions and solvation effects. While these biases still allow human experts to compete with blind algorithms in some areas, the underutilization of the information leads to significantly worse performance in data-rich tasks such as binding affinity prediction.

Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4.

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

MathDL: mathematical deep learning for D3R Grand Challenge 4.

We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranking and free energy estimation for Cathepsin S (CatS). We have developed advanced mathematics, namely differential geometry, algebraic graph, and/or algebraic topology, to accurately and efficiently encode high dimensional physical/chemical interactions into scalable low-dimensional rotational and translational invariant representations. These representations are integrated with deep learning models, such as generative adversarial networks (GAN) and convolutional neural networks (CNN) for pose prediction and energy evaluation, respectively. Overall, our MathDL models achieved the top place in pose prediction for BACE ligands in Stage 1a. Moreover, our submissions obtained the highest Spearman correlation coefficient on the affinity ranking of 460 CatS compounds, and the smallest centered root mean square error on the free energy set of 39 CatS molecules. It is worthy to mention that our method on docking pose predictions has significantly improved from our previous ones.

Biased signaling agonist of dopamine D3 receptor induces receptor internalization independent of ß-arrestin recruitment.

Agonist-induced internalization of G protein-coupled receptors (GPCRs) is a significant step in receptor kinetics and is known to be involved in receptor down-regulation. However, the dopamine D3 receptor (D3R) has been an exception wherein agonist induces D3Rs to undergo desensitization followed by pharmacological sequestration - which is defined as the sequestration of cell surface receptors into a more hydrophobic fraction within the plasma membrane without undergoing the process of receptor internalization. Pharmacological sequestration renders the receptor in an inactive state on the membrane. In our previous study we demonstrated that a novel class of D3R agonists exemplified by SK608 have biased signaling properties via the G-protein dependent pathway and do not induce D3R desensitization. In this study, using radioligand binding assay, immunoblot or immunocytochemistry methods, we observed that SK608 induced internalization of human D3R stably expressed in CHO, HEK and SH-SY5Y cells which are derived from neuroblastoma cells, suggesting that it is not a cell-type specific event. Further, we have evaluated the potential mechanism of D3R internalization induced by these biased signaling agonists. SK608-induced D3R internalization was time- and concentration-dependent. In comparison, dopamine induced D3R upregulation and pharmacological sequestration in the same assays. GRK2 and clathrin/dynamin I/II are the key molecular players in the SK608-induced D3R internalization process, while ß-arrestin 1/2 and GRK-interacting protein 1(GIT1) are not involved. These results suggest that SK608-promoted D3R internalization is similar to the type II internalization observed among peptide binding GPCRs.

Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.

In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic.