Vol 20, No 1 (2024)

Background:To our knowledge, there is still a lack of scientific reports on the pharmacological mechanism of the Zuogui Pill (ZGP) for treating osteoporosis (OP).

Aims:This study aimed to explore it via network pharmacology and molecular docking.

Methods:We identified active compounds and associated targets in ZGP via two drug databases. Disease targets of OP were obtained utilizing five disease databases. Networks were established and analyzed through the Cytoscape software and STRING databases. Enrichment analyses were performed using the DAVID online tools. Molecular docking was performed using Maestro, PyMOL, and Discovery Studio software.

Results:89 drug active compounds, 365 drug targets, 2514 disease targets, and 163 drug-disease common targets were obtained. Quercetin, kaempferol, phenylalanine, isorhamnetin, betavulgarin, and glycitein may be the crucial compounds of ZGP in treating OP. AKT1, MAPK14, RELA, TNF, and JUN may be the most important therapeutic targets. Osteoclast differentiation, TNF, MAPK, and thyroid hormone signaling pathways may be the critical therapeutic signaling pathways. The potential therapeutic mechanism mainly relates to osteoblastic or osteoclastic differentiation, oxidative stress, and osteoclastic apoptosis.

Conclusion:This study has revealed the anti-OP mechanism of ZGP, which offers objective evidence for relevant clinical application and further basic research.

Background:There has been a growing interest in discovering a viable drug for the new coronavirus (SARS-CoV-2) since the beginning of the pandemic. Protein-ligand interaction studies are a crucial step in the drug discovery process, as it helps us narrow the search space for potential ligands with high drug-likeness. Derivatives of popular drugs like Remdesivir generated through tools employing evolutionary algorithms are usually considered potential candidates. However, screening promising molecules from such a large search space is difficult. In a conventional screening process, for each ligand-target pair, there are time-consuming interaction studies that use docking simulations before downstream tasks like thermodynamic, kinetic, and electrostatic-potential evaluation.

Objective:This work aims to build a model based on deep learning applied over the graph structure of the molecules to accelerate the screening process for novel potential candidates for SARS-CoV-2 by predicting the binding energy of the protein-ligand complex.

Methods:In this work, ‘Graph Convolutional Capsule Regression’ (GCCR), a model which uses Capsule Neural Networks (CapsNet) and Graph Convolutional Networks (GCN) to predict the binding energy of a protein-ligand complex is being proposed. The model’s predictions were further validated with kinetic and free energy studies like Molecular Dynamics (MD) for kinetic stability and MM/GBSA analysis for free energy calculations.

Results:The GCCR showed an RMSE value of 0.0978 for 81.3% of the concordance index. The RMSE of GCCR converged around the iteration of just 50 epochs scoring a lower RMSE than GCN and GAT. When training with Davis Dataset, GCCR gave an RMSE score of 0.3806 with a CI score of 87.5%.

Conclusion:The proposed GCCR model shows great potential in improving the screening process based on binding affinity and outperforms baseline machine learning models like DeepDTA, KronRLS, Sim- Boost, and other Graph Neural Networks (GNN) based models like Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).

Background:Caulis Spatholobiis one of the necessary Chinese herbal medicines for hematologists in the treatment of malignant tumors, but its potential targets and molecular mechanisms need further exploration.

Objective:This study aimed to predict the relevant targets of the treatment of chronic myeloid leukemia (CML) with Caulis Spatholobi by applying the network pharmacology method, and in vitro cell experiments were conducted to verify the mechanism of Caulis Spatholobi in the treatment of CML.

Methods:TCMSP, ETCM, Genecards, and GisGeNET databases were used to obtain relevant targets of Caulis Spatholobi in the treatment of CML. Go and KEGG analyses were performed using the David database. Using Cytoscape 3.7.2, the \"active compounds-targets-pathways\" network was constructed. Further validation was carried out by pharmacological experiments in vitro. The proliferation and apoptosis of K562 cells were observed by the MTT method and Hoechst 33242 fluorescence staining method. The predicted targets and their related signal pathways were verified by western blotting.

Results:In this study, 18 active compounds and 43 potential targets were obtained. The results of the MTT method showed that compared with the normal control group, 62.5-500 µg/mL alcohol extract of Caulis Spatholobi had an obvious inhibitory effect on K562 and the IC50 value was less than 100 µg/mL. The Hoechst 33242 fluorescence staining method showed that the alcohol extract of Caulis Spatholobi could promote apoptosis. The results of western blotting showed that compared with the normal control group, the expressions of Bax and Caspase-3 proteins in the 62.5 and 125 µg/mL alcohol extract of Caulis Spatholobi groups were significantly up-regulated (p < 0.05). The expression of Bcl-2 in the 125 µg/mL alcohol extract of the Caulis Spatholobi group was significantly down-regulated (p < 0.01), and the expression of Bcl-2 in the 62.5 and 31.25 µg/mL alcohol extract of Caulis Spatholobi groups was also significantly down-regulated (p < 0.05). It showed that the ethanol extract of Caulis Spatholobus could promote apoptosis by up-regulating the expression of Bax and caspase-3 and down-regulating the expression of the Bcl-2 protein.

Conclusion:The treatment of Caulis Spatholobi for CML has the characteristics of multi-targets and multi-pathways. The results of in vitro pharmacological experiments demonstrated that its mechanism of action might be based on the expression of key target proteins, such as Caspase-3, Bcl-2, and Bax, thereby inhibiting cell proliferation and promoting cell apoptosis, which provides a scientific basis for the treatment of CML.

Introduction:Integration of viral DNA into the host cell genome, carried out by the HTLV-1 integrase enzyme, is a crucial step in the Human T-lymphotropic virus type I (HTLV-1) life cycle. Thus, HTLV-1 integrase is considered an attractive therapeutic target; however, no clinically effective inhibitors are available to treat HTLV-1 infection.

Objective:The main objective was to identify potential drug-like compounds capable of effectively inhibiting HTLV-1 integrase activity.

Methods:In this study, a model of HTLV-1 integrase structure and three integrase inhibitors (dolutegravir, raltegravir, and elvitegravir as scaffolds) were used for designing new inhibitors. Designed molecules were used as templates for virtual screening to retrieve new inhibitors from PubChem, ZINC15, and ChEMBL databases. Drug-likeness and docked energy of the molecules were investigated using the SWISS-ADME portal and GOLD software. Stability and binding energy of the complexes were further investigated using molecular dynamic (MD) simulation.

Results:Four novel potential inhibitors were developed using a structure-based design protocol and three compounds from virtual screening. They formed hydrogen bonding interactions with critical residues Asp69, Asp12, Tyr96, Tyr143, Gln146, Ile13, and Glu105. In addition, π stacking, halogen, and hydrogen bond interactions were seen between compounds (especially halogenated benzyl moieties) and viral DNA similar to those seen in parent molecules. MD simulation confirmed higher stability of the receptor-ligand complex than the ligand-free enzyme.

Conclusion:Combing structure-based design and virtual screening resulted in identifying three drug-like molecules (PubChem CID_138739497, _70381610, and _140084032) that are suggested as lead compounds for developing effective drugs targeting HTLV-1 integrase enzyme.