Significantly different effects of tetrahydroberberrubine enantiomers on dopamine D1/D2 receptors revealed by experimental study and integrated in silico simulation

The release of host-derived antibodies bound to the variant surface glycoprotein (VSG) of Trypanosoma brucei cannot be explained by pH-dependent conformational changes of the VSG dimer

Background

Trypanosoma brucei is a protozoan parasite that evades the mammalian host's adaptive immune response by antigenic variation of the highly immunogenic variant surface glycoprotein (VSG). VSGs form a dense surface coat that is constantly recycled through the endosomal system. Bound antibodies are separated in the endosome from the VSG and destroyed in the lysosome. For VSGs it has been hypothesized that pH-dependent structural changes of the VSG could occur in the more acidic environment of the endosome and hence, facilitate the separation of the antibody from the VSG.

Methods

We used size exclusion chromatography, where molecules are separated according to their hydrodynamic radius to see if the VSG is present as a homodimer at both pH values. To gain information about the structural integrity of the protein we used circular dichroism spectroscopy by exposing the VSG in solution to a mixture of right- and left-circularly polarized light and analysing the absorbed UV spectra. Evaluation of protein stability and molecular dynamics simulations at different pH values was performed using different computational methods.

Results

We show, for an A2-type VSG, that the dimer size is only slightly larger at pH 5.2 than at pH 7.4. Moreover, the dimer was marginally more stable at lower pH due to the higher affinity (ΔG = 353.37 kcal/mol) between the monomers. Due to the larger size, the predicted epitopes were more exposed to the solvent at low pH. Moderate conformational changes (ΔRMSD = 0.35 nm) in VSG were detected between the dimers at pH 5.2 and pH 7.4 in molecular dynamics simulations, and no significant differences in the protein secondary structure were observed by circular dichroism spectroscopy.

Conclusions

Thus, the dissociation of anti-VSG-antibodies in endosomes cannot be explained by changes in pH.

Methyleugenol Has an Antidepressant Effect in a Neuroendocrine Model: In Silico and In Vivo Evidence

Major depressive disorder is a severe mood disorder characterized by different emotions and feelings. This study investigated the antidepressant activity of the phenylpropanoid methyleugenol (ME) in adult female mice exposed to a stress model induced by dexamethasone. The animals were randomly divided into groups containing eight animals and were pre-administered with dexamethasone (64 μg/kg subcutaneously). After 165 and 180 min, they were treated with ME (25, 50 and 100 mg/kg intraperitoneally) or imipramine (10 mg/kg intraperitoneally) after 45 min and 30 min, respectively; they were then submitted to tests which were filmed. The videos were analyzed blindly. In the tail suspension test, ME (50 mg/kg) increased latency and reduced immobility time. In the splash test, ME (50 mg/kg) decreased grooming latency and increased grooming time. In the open field, there was no statistical difference for the ME groups regarding the number of crosses, and ME (50 mg/kg) increased the number of rearing and time spent in the center. Regarding in silico studies, ME interacted with dopaminergic D1 and α1 adrenergic pathway receptors and with tryptophan hydroxylase inhibitor. In the in vivo evaluation of the pathways of action, the antidepressant potential of ME (50 mg/kg) was reversed by SCH23390 (4 mg/kg intraperitoneally) dopaminergic D1 receptor, Prazosin (1 mg/kg intraperitoneally) α1 adrenergic receptor, and PCPA (4 mg/kg intraperitoneally) tryptophan hydroxylase inhibitor. Our findings indicate that ME did not alter with the locomotor activity of the animals and shows antidepressant activity in female mice with the participation of the D1, α1 and serotonergic systems.

Computational insights into ligand-induced G protein and β-arrestin signaling of the dopamine D1 receptor

The dopamine D1 receptor (D1R), is a class A G protein coupled-receptor (GPCR) which has been a promising drug target for psychiatric and neurological disorders such as Parkinson's disease (PD). Previous studies have suggested that therapeutic effects can be realized by targeting the β-arrestin signaling pathway of dopamine receptors, while overactivation of the G protein-dependent pathways leads to side effects, such as dyskinesias. Therefore, it is highly desirable to develop a D1R ligand that selectively regulates the β-arrestin pathway. Currently, most D1R agonists are signaling-balanced and stimulate both G protein and β-arrestin pathways, with a few reports of G protein biased ligands. However, identification and characterization of β-arrestin biased D1R agonists has been a challenge thus far. In this study, we implemented Gaussian accelerated molecular dynamics (GaMD) simulations to provide valuable computational insights into the possible underlying molecular mechanism of the different signaling properties of two catechol and two non-catechol D1R agonists that are either G protein biased or signaling-balanced. Dynamic network analysis further identified critical residues in the allosteric signaling network of D1R for each ligand at different conformational or binding states. Some of these residues are crucial for G protein or arrestin signals of GPCRs based on previous studies. Finally, we provided a molecular design strategy which can be utilized by medicinal chemists to develop potential β-arrestin biased D1R ligands. The proposed hypotheses are experimentally testable and can guide the development of safer and more effective medications for a variety of CNS disorders.

Effect of Berberine Hydrochloride on the Diversity of Intestinal Flora in Parkinson's Disease Patients

This study aimed to investigate the effect of berberine hydrochloride on the diversity of intestinal flora in patients with Parkinson's disease (PD). Prospectively selected 68 PD patients, admitted to our hospital from April 2021 to June 2021, were randomly assigned to an observation group and a control group (n = 34 per group). Patients in the control group were given conventional treatment in accordance with Parkinson's diagnosis and treatment guidelines. Patients in the observation group were administered berberine hydrochloride besides the treatment in the control group. After continuous treatment for 3 months, the enzyme-linked immunosorbent assay was applied to determine interleukin-8 (IL-8), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) levels. High-throughput sequencing technology was employed to perform DNA sequencing on the 16S rRNA genes of all bacteria in stool samples before and after treatment in the two groups to analyze the distribution of intestinal flora. After treatment, the levels of IL-8, IL-6, and TNF-α were lower in the observation group than those in the control group (P < 0.05). Regarding intestinal flora, the Chao index, Ace index, and Shannon index were higher in the observation group than those in the control group. The Simpson index was lower in the observation group than that of the control group (P < 0.05). The principal component analysis chart analysis exhibited that the overall structure of the intestinal flora was quite different between the observation and the control groups after treatment. Berberine hydrochloride can improve the disorder of intestinal flora in PD patients and suppress the expression of inflammatory factors.

Generative chemistry: drug discovery with deep learning generative models

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.