Current computer-aided drug design最新文献

Background: Rheumatoid Arthritis (RA) is a chronic autoimmune disease with various symptoms in patients. Duhuo Jisheng Decoction (DHJSD) has been used to treat RA in China for a long history as a classic TCM formula. However, the underlying pharmacological mechanism still needs to be elucidated.

Purpose: In the current study, we combined network pharmacology with molecular docking to investigate the potential mechanism of DHJSD treating RA.

Methods: The active compounds and related targets of DHJSD were obtained from the TCMSP database. The RA targets were retrieved from the GEO database. The PPI network of overlapping targets was constructed, whereas the core genes were selected by CytoNCA for molecular docking. GO and KEGG enrichment analysis were used to further explore the biological process and pathways of overlapping targets. On this basis, molecular docking was carried out to verify the interrelations of the main compounds and core targets.

Results: In this study, we found 81 active components corresponding to 225 targets of DHJSD. Moreover, 775 RA-related targets were obtained, of which 12 were shared between DHJSD targets and RA target genes. From GO and KEGG analysis, there were 346 GO items and 18 signaling pathways. As the molecular docking showed, the binding of components was stable with the core gene.

Conclusion: In conclusion, our works revealed the underlying mechanism of DHJSD for treating RA using network pharmacology and molecular docking, which provided a theoretical basis for further clinical application in the future.

Background: Plasmodium vivax (P. vivax) is one of the highly prevalent human malaria parasites. Due to the presence of extravascular reservoirs, P. vivax is extremely challenging to manage and eradicate. Traditionally, flavonoids have been widely used to combat various diseases. Recently, biflavonoids were discovered to be effective against Plasmodium falciparum.

Methods: In this study, in silico approaches were utilized to inhibit Duffy binding protein (DBP), responsible for Plasmodium invasion into red blood cells (RBC). The interaction of flavonoid molecules with the Duffy antigen receptor for chemokines (DARC) binding site of DBP was investigated using a molecular docking approach. Furthermore, molecular dynamic simulation studies were carried out to study the stability of top-docked complexes.

Results: The results showed the effectiveness of flavonoids, such as daidzein, genistein, kaempferol, and quercetin, in the DBP binding site. These flavonoids were found to bind in the active region of DBP. Furthermore, the stability of these four ligands was maintained throughout the 50 ns simulation, maintaining stable hydrogen bond formation with the active site residues of DBP.

Conclusion: The present study suggests that flavonoids might be good candidates and novel agents against DBP-mediated RBC invasion of P. vivax and can be further analyzed in in vitro studies.

Background: The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state.

Objective: This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state.

Methods: In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD.

Results: Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state.

Conclusion: The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Background: Type 2 diabetes-associated cognitive dysfunction (DCD) is a chronic complication of diabetes that has gained international attention. The medicinal compound Banxia Xiexin Decoction (BXXXD) from traditional Chinese medicine (TCM) has shown potential in improving insulin resistance, regulating endoplasmic reticulum stress (ERS), and inhibiting cell apoptosis through various pathways. However, the specific mechanism of action and medical value of BXXXD remain unclear.

Methods: We utilized TCMSP databases to screen the chemical constituents of BXXXD and identified DCD disease targets through relevant databases. By using Stitch and String databases, we imported the data into Cytoscape 3.8.0 software to construct a protein-protein interaction (PPI) network and subsequently identified core targets through network topology analysis. The core targets were subjected to Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The results were further validated through in vitro experiments.

Results: Network pharmacology analysis revealed the screening of 1490 DCD-related targets and 190 agents present in BXXXD. The topological analysis and enrichment analysis conducted using Cytoscape software identified 34 core targets. Additionally, GO and KEGG pathway analyses yielded 104 biological targets and 97 pathways, respectively. BXXXD exhibited its potential in treating DCD by controlling synaptic plasticity and conduction, suppressing apoptosis, reducing inflammation, and acting as an antioxidant. In a high glucose (HG) environment, the expression of JNK, Foxo3a, SIRT1, ATG7, Lamp2, and LC3 was downregulated. BXXXD intervention on HT22 cells potentially involved inhibiting excessive oxidative stress, promoting neuronal autophagy, and increasing the expression levels of JNK, SIRT1, Foxo3a, ATG7, Lamp2, and LC3. Furthermore, the neuroprotective effect of BXXXD was partially blocked by SP600125, while quercetin enhanced the favorable role of BXXXD in the HG environment.

Conclusion: BXXXD exerts its effects on DCD through multiple components, targets, levels, and pathways. It modulates the JNK/SIRT1/Foxo3a signaling pathway to mitigate autophagy inhibition and apoptotic damage in HT22 cells induced by HG. These findings provide valuable perspectives and concepts for future clinical trials and fundamental research.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo, and ex vivo experiments. As a result, numerous in silico computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Background: Shugan Jieyu Capsule (SJC) is a pure Chinese medicine compound prepared with Hypericum perforatum and Acanthopanacis senticosi. SJC has been approved for the clinical treatment of depression, but the mechanism of action is still unclear.

Objectives: Network pharmacology, molecular docking, and molecular dynamics simulation (MDS) were applied in the present study to explore the potential mechanism of SJC in the treatment of depression.

Methods: TCMSP, BATMAN-TCM, and HERB databases were used, and related literature was reviewed to screen the effective active ingredients of Hypericum perforatum and Acanthopanacis senticosi. TCMSP, BATMAN-TCM, HERB, and STITCH databases were used to predict the potential targets of effective active ingredients. GeneCards database, DisGeNET database, and GEO data set were used to obtain depression targets and clarify the intersection targets of SJC and depression. STRING database and Cytoscape software were used to build a protein-protein interaction (PPI) network of intersection targets and screen the core targets. The enrichment analysis on the intersection targets was conducted. Then the receiver operator characteristic (ROC) curve was constructed to verify the core targets. The pharmacokinetic characteristics of core active ingredients were predicted by SwissADME and pkCSM. Molecular docking was performed to verify the docking activity of the core active ingredients and core targets, and molecular dynamics simulations were performed to evaluate the accuracy of the docking complex.

Results: We obtained 15 active ingredients and 308 potential drug targets with quercetin, kaempferol, luteolin, and hyperforin as the core active ingredients. We obtained 3598 targets of depression and 193 intersection targets of SJC and depression. A total of 9 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2) were screened with Cytoscape 3.8.2 software. A total of 442 GO entries and 165 KEGG pathways (p <0.01) were obtained from the enrichment analysis of the intersection targets, mainly enriched in IL-17, TNF, and MAPK signaling pathways. The pharmacokinetic characteristics of the 4 core active ingredients indicated that they could play a role in SJC antidepressants with fewer side effects. Molecular docking showed that the 4 core active components could effectively bind to the 8 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2), which were related to depression by the ROC curve. MDS showed that the docking complex was stable.

Conclusion: SJC may treat depression by using active ingredients such as quercetin, kaempferol, luteolin, and hyperforin to regulate targets such as PTGS2 and CASP3 and signaling pathways such as IL-17, TNF, and MAPK, and participate in immune inflammation, oxidative stress, apoptosis, neurogenesis, etc.

Background: Based on the anti-inflammatory and analgesic activity of hydrazone and phthalimide, a new series of hybrid hydrazone and phthalimide pharmacophores was prepared and evaluated as analgesic agents.

Methods: The designed ligands were synthesized by reaction of the appropriate aldehydes and 2- aminophthalimide. Analgesic, cyclooxygenase inhibitory, and cytostatic activity of prepared compounds were measured.

Results: All the tested ligands demonstrated significant analgesic activity. Moreover, compounds 3i and 3h were the most potent ligands in the formalin and writhing tests, respectively. Compounds 3g, 3j, and 3l were the most COX-2 selective ligands and ligand 3e was the most potent COX inhibitor with a 0.79 of COX-2 selectivity ratio. The presence of electron-withdrawing moieties with hydrogen bonding ability at the meta position was found to affect the selectivity efficiently, in which compounds 3g, 3l, and 3k showed high COX-2 selectivity, and compound 3k was the most potent one. The cytostatic activity of selected ligands demonstrated that compounds 3e, 3f, 3h, 3k, and 3m showed good analgesic and COX inhibitory activity and were less toxic than the reference drug.

Conclusion: High therapeutic index of these ligands is one of the valuable advantages of these compounds.

Aims: The aim of this study is to develop a novel antiviral strategy capable of efficiently targeting a broad set of SARS-CoV-2 variants.

Background: Since the first emergence of SARS-CoV-2, it has rapidly transformed into a global pandemic, posing an unprecedented threat to public health. SARS-CoV-2 is prone to mutation and continues to evolve, leading to the emergence of new variants capable of escaping immune protection achieved due to previous SARS-CoV-2 infections or by vaccination.

Objective: RNA interference (RNAi) is a remarkable biological mechanism that can induce gene silencing by targeting complementary mRNA and inhibiting its translation.

Methods: In this study, using the computational approach, we predicted the most efficient siRNA capable of inhibiting SARS-CoV-2 variants of concern (VoCs).

Results: The presented siRNA was characterized and evaluated for its thermodynamic properties, offsite-target hits, and in silico validation by molecular docking and molecular dynamics simulations (MD) with Human AGO2 protein.

Conclusion: The study contributes to the possibility of designing and developing an effective response strategy against existing variants of concerns and preventing further.

Introduction: JAK3 kinase inhibitor has become an effective means to treat tumors and autoimmune diseases.

Methods: In this study, molecular docking and molecular dynamics simulation were used to study the theoretical interaction mechanism between 1-phenylimidazolidine-2-one molecules and JAK3 protein.

Results: The results of molecular docking showed that the six 1-phenylimidazolidine-2-one derivatives obtained by virtual screening were bound to the ATP pocket of JAK3 kinase, which were competitive inhibitors of ATP, and were mainly bound to the pocket through hydrogen bonding and hydrophobic interaction. Further, MM/GBSA based on molecular dynamics simulation sampling was used to calculate the binding energy between six molecules and the JAK3 kinase protein. Subsequently, the binding energy was decomposed into the contribution of each amino acid residue, of which Leu905, Lys855, Asp967, Leu956, Tyr904, and Val836 were the main energycontributing residues. Among them, the molecule numbered LCM01415405 can interact with the specific amino acid Arg911 of JAK3 kinase, suggesting that the molecule may be a selective JAK3 kinase inhibitor. The root-mean-square fluctuation (RMSF) of JAK3 kinase pocket residues during molecular dynamics simulation showed that the combination of six new potential small molecule inhibitors with JAK3 kinase could reduce the flexibility of JAK3 kinase pocket residues.

Conclusion: These findings reveal the mechanism of 1-phenylimidazolidine-2-one derivatives on JAK3 protein and provide a relatively solid theoretical basis for the development and structural optimization of JAK3 protein inhibitors.

Background: Polygonum cuspidatum (PC), a widely used Chinese herbal medicine (CHM), plays an important role in treating various diseases including osteoarthritis (OA). Yet, the multicomponent and multitarget characteristics of PC make deciphering the pharmacological mechanisms difficult.

Objective: The purpose of this study is to identify the core molecular mechanisms of PC against OA.

Methods: The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to search for the active ingredients of PC. GeneCards was then screened to establish relevant databases for OA. A visual interactive network diagram of the relationship between the active ingredient, action target, and disease was built using Uniprot. Finally, we used STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database to explain the interaction network of proteins and to further analyze the relationships between related proteins.

Results: PC was screened for nine potentially effective active compounds that can be used to treat OA: 6,8-Dihydroxy-7-methoxyxanthone, rhein, physovenine, beta-sitosterol, picralinal, quercetin, luteolin, catechin, and resveratrol. Using GeneCards database and TCMSP database, we obtained 149 OA-related genes after taking the intersection of OA and PC targets. Moreover, eight core target proteins were calculated by CytoNCA plugin, which is used for network centrality analysis. The enrichment analysis of the common target genes shared by PC and OA unraveled the main biological processes, such as responses to lipopolysaccharide, chemical stress, and reactive oxygen species. Previous research has demonstrated that signaling pathways related to apoptosis, inflammation, and cartilage protection are involved with those core target genes we found, like TNF and PI3K-Akt signaling pathways. The results bring that PC similarly has the potential to treat OA.

Conclusion: The main purpose of this study is to screen the active ingredients and most important target molecules of PC in treating OA. This was achieved using bioinformatic tools and databases to investigate molecular docking technology. The findings provide a theoretical foundation and potential new treatment plan for OA using PC.