The 3D Structure of the Binding Pocket of the Human Oxytocin Receptor for Benzoxazine Antagonists, Determined by Molecular Docking, Scoring Functions and 3D-QSAR Methods

Combining transcriptomic analysis and network pharmacology to explore the mechanism by which Shaofu Zhuyu decoction improves diabetes mellitus erectile dysfunction

BACKGROUND

Erectile dysfunction is common among the complications of diabetes mellitus. Shaofu Zhuyu decoction (SFZYD) is commonly used to treat diabetic mellitus erectile dysfunction (DMED). However, its main active components and specific mechanism are still unknown.

PURPOSE

To confirm the activity of SFZYD in improving DMED, explore the main active components of SFZYD, and clarify the underlying mechanism.

METHODS

A diabetic rat model was induced with streptozotocin (STZ). After intragastric administration, erectile function was assessed by the maximum intracavernous pressure (ICPmax)/mean arterial pressure (MAP). Corpus cavernosum fibrosis was evaluated by Masson staining, and ELISA methods were used to determine the serum levels of IL-6, TNF-α, IL-10, IL-4 and IL-1β to evaluate inflammation. Then, the main active components of SFZYD were identified by UPLC‒MS/MS. Finally, the target and biological mechanism of SFZYD in improving DMED were predicted by combined network pharmacology and transcriptomics, which was also validated by molecular docking and cellular thermal shift assay (CETSA) experiments.

RESULTS

SFZYD significantly improved erectile dysfunction and inhibited inflammatory responses and local tissue fibrosis in diabetic rats. A total of 1846 active components were identified by UPLC‒MS/MS, and isorhamnetin was the main active component. The transcriptomic results were used to identify differentially expressed genes among the control, DM and SFZYD groups, and 1264 differentially expressed genes were obtained from the intersection. The network pharmacology results showed that SFZYD acts on core targets such as AKT1, ALB, HSP90AA1 and ESR1 through core components such as isorhamnetin, quercetin and chrysophanic acid. Further combined analysis revealed that multiple targets, such as CYP1B1, DPP4, NOS2 and LCN2, as well as the regulation of the PI3K-AKT signaling pathway, may be important mechanisms by which SFZYD improves DMED. Molecular docking verification showed that isorhamnetin, the key component of SFZYD, has good binding ability with several core targets, and its binding ability with CYP1B1 was the strongest. The CETSA results showed that isorhamnetin binds to CYP1B1 in CCECs.

CONCLUSION

SFZYD improves DMED, inhibits the inflammatory response and alleviates local tissue fibrosis. The combined application of transcriptomic, network pharmacology, molecular docking and CETSA approaches was helpful for revealing the mechanism by which SFZYD improves DMED, which may be related to the regulation of CYP1B1 and the PI3K-Akt signaling pathway.

In silico profiling the interaction mechanism of 2,5-diketopiperazine derivatives as oxytocin antagonists

Oxytocin plays a vital role in the occurrence of preterm birth by interacting with oxytocin receptor (OTR), and thus OTR antagonists provide effective approaches for the treatment of early birth. Presently, for purpose of exploring the structural traits affecting the antagonism potency, the up-to-date largest set of 121 2,5-diketopiperazine derivatives as OTR antagonists was subjected to ligand-based three-dimensional quantitative structure-activity (3D-QSAR) analysis applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The resultant optimal CoMSIA model displays proper validity and predictability with cross-validated correlation coefficient Q² = 0.614, non-cross-validated correlation coefficient R²_ncv = 0.969 and predicted correlation coefficient R²_pre = 0.912 for the test set of compounds, respectively. In addition, docking study was carried out for further elucidating the binding modes of OTR antagonists. The final docking cavity was located among the TM2-TM7 helices of the target protein and proved to be the same as described by other scholars' researches of other type of OTR antagonists. The major amino acids forming the cavity are Q92, Q96, K116, Q119, V120, M123, G196, I201, Q295, W288, F311 and M315. Representative compound 118 forms two H-bonds with Q119 and two H-bonds with Q295, with also a π - π stacking effect with F311, respectively. Meanwhile, as a supplementary tool to study the antagonists' conformation in the binding cavity, molecular dynamics (MD) simulation was also performed to further elucidate the changes in the ligand-receptor complex. We hope that the obtained models and information may help to provide an insight into the interaction mechanism of OTR antagonists and facilitate the design and optimization of novel antagonists for preventing premature birth.

The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

INTRODUCTION

The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.

Molecular docking and comparative molecular similarity indices analysis of estrogenicity of polybrominated diphenyl ethers and their analogues

Molecular docking and three-dimensional quantitative structure-activity relationships (3D-QSAR) were used to develop models to predict estrogenicity of polybrominated diphenyl ethers (PBDEs), para-hydroxylated polybrominated diphenyl ethers (para-HO-PBDEs), and brominated bisphenol A compounds to the human estrogen receptor alpha (hERalpha). Based on the molecular conformations developed from the molecular docking, predictive comparative molecular similarity indices analysis (CoMSIA) models were developed. The results of CoMSIA modeling with region focusing included were: leave-one-out (LOO) cross-validated coefficient q(2)(LOO) = 0.722 (all 26 compounds), q(2)(LOO) = 0.633 (the training set, 20 compounds), q(2)(LMO, two groups) = 0.520 +/- 0.155 (26 compounds), q(2)(LMO, five groups) = 0.665 +/- 0.068 (26 compounds), predictive r(2), r(2)(pred) = 0.686 (the test set, 6 compounds), and Q(2)(EXT) = 0.678. The 3D-QSAR can be used to infer the activities of compounds with similar structural characteristics. The interaction mechanism between compounds and the hERalpha was explored. Hydrogen bonding of the compound with Glu353 in the hERalpha is an important determinant of the estrogenic activity of para-HO-PBDEs and brominated bisphenol A.

Possible dynamic anchor points in a benzoxazinone derivative-human oxytocin receptor system--a molecular docking and dynamics calculation

In this study, we performed a molecular docking and dynamics simulation for a benzoxazinone-human oxytocin receptor system to determine the possible hydrophobic and electrostatic interaction points in the dynamic complex. After the homology modeling, the ligand was docked into the putative active using AutoDock 3.05. After the application of energetic and structural filters, the complexes obtained were further refined with a simulated annealing protocol (AMBER8) to remove steric clashes. Three complexes were selected for subjection to the molecular dynamics simulation (5 ns), and the results on the occurrence of average anchor points showed a stable complex between the benzoxazinone derivative and the receptor. The complex could be used as a good starting point for further analysis with site-directed mutagenesis, or further computational research.

Receptor-based QSAR studies of non-peptide human oxytocin receptor antagonists

In the present study, QSAR calculations were performed on the receptor-based alignment of 58 non-peptide human oxytocin receptor antagonists. With the aid of different scoring functions (AutoDock 3.05 built-in and X-Score 1.2) the evolved receptor-ligand complexes were characterized. By means of various datasets it was confirmed that the scoring functions were not capable to predict the biological activity correctly in compounds containing a rigid derivative in the variable region. To improve the pKi prediction 3D-QSAR calculation was performed. The regions related to the biological activity were determined by using cross-validated r2(q2)-guided region selection (q2-GRS) method. The predictive power of the CoMFA model [r(pred)2=0.89, q2(LMO, five groups)=0.695+/-0.034] allowed prediction of the biological activities of newly synthesized compounds and confirmed the receptor-based alignment.

Three dimensional model of severe acute respiratory syndrome coronavirus helicase ATPase catalytic domain and molecular design of severe acute respiratory syndrome coronavirus helicase inhibitors

The modeling of the severe acute respiratory syndrome coronavirus helicase ATPase catalytic domain was performed using the protein structure prediction Meta Server and the 3D Jury method for model selection, which resulted in the identification of 1JPR, 1UAA and 1W36 PDB structures as suitable templates for creating a full atom 3D model. This model was further utilized to design small molecules that are expected to block an ATPase catalytic pocket thus inhibit the enzymatic activity. Binding sites for various functional groups were identified in a series of molecular dynamics calculation. Their positions in the catalytic pocket were used as constraints in the Cambridge structural database search for molecules having the pharmacophores that interacted most strongly with the enzyme in a desired position. The subsequent MD simulations followed by calculations of binding energies of the designed molecules were compared to ATP identifying the most successful candidates, for likely inhibitors - molecules possessing two phosphonic acid moieties at distal ends of the molecule.