Ginsenoside Rb1 as a multi-target modulator in heart failure: Mechanistic insights into extracellular remodeling and transcriptional pathways from network pharmacology, molecular dynamics, and binding free energy analyses.

Molecular dynamics simulations reveal the allosteric effect of F1174C resistance mutation to ceritinib in ALK-associated lung cancer

The current study investigates the effectiveness of phytocompounds from the whole green jackfruit flour methanol extract (JME) against obesity-linked diabetes mellitus using integrated network pharmacology and molecular modeling approach. Through network pharmacology, druglikeness and pharmacokinetics, molecular docking simulations, GO analysis, molecular dynamics simulations, and binding free energy analyses, it aims to look into the mechanism of the JME phytocompounds in the amelioration of obesity-linked diabetes mellitus. There are 15 predicted genes corresponding to the 11 oral bioactive compounds of JME. The most important of these 15 genes was MAPK3. According to the network analysis, the insulin signaling pathway has been predicted to have the strongest affinity to MAPK3 protein, which was chosen as the target. With regard to the molecular docking simulation, the greatest notable binding affinity for MAPK3 was discovered to be caffeic acid (-8.0 kJ/mol), deoxysappanone B 7,3’-dimethyl ether acetate (DBDEA) (-8.2 kJ/mol), and syringic acid (-8.5 kJ/mol). All the compounds were found to be stable inside the inhibitor binding pocket of the enzyme during molecular dynamics simulation. During binding free energy calculation, all the compounds chiefly used Van der Waal’s free energy to bind with the target protein (caffeic acid: 102.296 kJ/mol, DBDEA: -104.268 kJ/mol, syringic acid: -100.171 kJ/mol). Based on these findings, it may be inferred that the reported JME phytocompounds could be used for in vitro and in vivo research, with the goal of targeting MAPK3 inhibition for the treatment of obesity-linked diabetes mellitus.

Binding mechanism of CDK5 with roscovitine derivatives based on molecular dynamics simulations and MM/PBSA methods

Increasing evidence suggests that development of heart failure involves activation of stress-response inflammatory cytokines, including tumor necrosis factor-alpha and interleukin-6. Yet, the myocyte contribution to their induction in failing hearts and the underlying regulatory mechanism in stressed myocardium remain unclear. In cultured cardiac myocytes, specific activation of stress-activated mitogen-activated protein kinase, p38, by upstream activator MKK6bE led to significant induction of tumor necrosis factor-alpha and interleukin-6 secretion, whereas treating cells with a selective p38 inhibitor (SB239068) significantly blocked the cytokine secretion from myocytes and increased their intracellular accumulation. Targeted expression of MKK6bE in transgenic hearts also resulted in a marked elevation in plasma tumor necrosis factor-alpha and interleukin-6; oral administration of SB239068 resulted in a significant reduction in their plasma levels but an increase in intracardiac accumulation of both cytokines. MKK6bE transgenic hearts developed marked interstitial fibrosis with increased matrix metalloproteinase abundance and selective induction of tissue inhibitor of matrix metalloproteinase-1; this extracellular matrix remodeling was also significantly attenuated by p38 inhibition. Along with cytokine induction and extracellular remodeling, MKK6bE transgenic animals displayed impaired hemodynamic function, whereas p38 inhibition improved the cardiac performance and prolonged the survival of the animals. Stress-activated p38 kinase is a critical regulator of inflammatory response in cardiomyocytes with significant contribution to pathological remodeling in stressed myocardium. Inhibition of p38 may represent a useful therapeutic avenue to ameliorate cardiac pathology and heart failure evolution.

Background: Heart failure (HF), the terminal stage of cardiovascular disease with high morbidity and mortality, remains poorly managed by current therapies. Ventricular remodeling in HF is fundamentally characterized by myocardial fibrosis. While ginsenoside Rb1 has demonstrated anti-fibrotic effects in HF, the underlying mechanism remains unclear. Twist1, an upstream regulator of energy metabolism factors PGC-1α and PPARα, may attenuate fibrosis by preserving systemic energy homeostasis, suggesting its pivotal role in HF pathogenesis. This study explores ginsenoside Rb1's anti-HF mechanisms through the regulation of ginsenoside Rb1 on these metabolic regulators. Methods: Sprague Dawley rats were subjected to a ligation of the left anterior descending coronary artery to induce an HF model, followed by ginsenoside Rb1 treatment for 6 weeks. Therapeutic effects were evaluated through cardiac function assessment, myocardial histopathological staining (HE, Masson, immunofluorescence, immunohistochemistry), mitochondrial morphology observation (transmission electron microscopy), energy metabolism analysis (electron transport chain efficiency, mitochondrial membrane potential, ATP content), and protein expression profiling (Twist1, PGC-1α, PPARα, GLUT4, PPARγ). Additionally, H9c2 cells induced with endothelin-1 to model HF were employed as an in vitro model to further investigate ginsenoside Rb1's regulatory effects on the Twist1/PGC-1α/PPARα signaling pathway. Results: Ginsenoside Rb1 can restore cardiac function in HF rats, improve mitochondrial function, alleviate energy metabolism disorders, and inhibit ventricular remodeling. By modulating the Twist1/PGC-1α/PPARα signaling pathway, ginsenoside Rb1 suppressed the abnormal overexpression of Twist1 and maintained normal expression of downstream PGC-1α and PPARα. In vitro experiments further demonstrated that ginsenoside Rb1 significantly inhibited Twist1 expression in H9c2 cardiomyocytes with HF while promoting PGC-1α and PPARα expression, thereby restoring myocardial energy metabolism and mitigating ventricular remodeling in HF. Conclusions: Ginsenoside Rb1 can inhibit the upregulation of Twist1 and activate the expression of its downstream PGC-1α and PPARα expression, by modulating the Twist1/PGC-1α/PPARα signaling pathway, alleviating ventricular remodeling in HF patients and improving myocardial energy metabolism dysfunction. Twist1 may be a key target for the treatment of HF. This study not only elucidates the mechanism by which ginsenoside Rb1 alleviates HF, but also provides new insights into the clinical treatment of HF.

Objective: Yiqi Fumai Lyophilized Injection (YQFM), a Chinese medicine injection, has been widely used for the treatment of cardiovascular diseases, especially heart failure (HF). However, bioactive compounds and underlying mechanisms of YQFM in treating HF remain poorly understood. Materials and Methods: Network pharmacology was employed to investigate the bioactive compounds and mechanisms of YQFM. A compound-target network was constructed to screen bioactive compounds based on contribution index calculation. Then, an adriamycin-induced HF rat model was established to evaluate the cardio-protective effects of YQFM by hematoxylin and eosin staining and enzyme-linked immunosorbent assays. Results: Network pharmacology indicated that YQFM may alleviate HF through 36 compounds and 109 targets. Particularly, ginsenosides Rb1, Rg1, Re, Rf, Rb2, Rh1, schisandrin, and ginsenoside Rc were indicated as the top contributors of YQFM in treating HF. YQFM was predicted to act on multiple targets such as vascular endothelial growth factor A, interleukin-2 (IL-2), IL-6, and IL-1β, as well as to regulate signaling pathways such as hypoxia-inducible factor 1, tumor necrosis factor, VEGF, and PI3K-Akt. The pharmacological study suggested that YQFM could attenuate cardiac injury and up-regulate plasma concentrations of VEGFR-1 and NO in HF rats. Ginsenoside Rb1, as the major contributor from network pharmacology analysis, also showed a cardioprotective effect and up-regulation of VEGFR-1 in plasma. Conclusions: Ginsenosides and schisandrin were predicted as the most important contributors to the cardioprotective effect of YQMF. Ginsenoside Rb1 was proved to alleviate HF and increase the plasma concentration of VEGFR-1.

Free Energy Calculations of Sparsomycin Analogs Binding to the Ribosome with Molecular Dynamics Simulations

Calculations of the binding free energy of various nonpolar aromatic ligands with the L99A mutant of T4 lysozyme using molecular dynamics (MD) simulation are presented. To ensure better convergence, biasing potentials are used to restrain the ligand orientation and center-of-mass movement relative to the binding site when the ligand is decoupled from its environment in the binding pocket. The bias introduced by the restraint potentials is removed once the ligand fully interacts with the rest of the system and the calculated binding free energy is independent of the applied restraints. To decrease the computational cost, the simulations are generated with a reduced system in which protein and water atoms within a 15 Å-radius sphere around the ligand are included explicitly, while the rest of the system is treated with the generalized solvent boundary potential (GSBP). For all the ligands, the precision of the calculated free energy is less than 0.5 kcal/mol. For small nonpolar ligands such as benzene, toluene, and ethylbenzene, the calculated binding free energies are within 1.1 kcal/mol of the experimental values. For larger ligands, the computed binding free energies are slightly more favorable than the experimental values. The nonbinding polar molecule, phenol, has a calculated binding free energy of -0.88 kcal/mol. The simulation protocol presented here provides a way to calculate the binding free energy of small molecules to receptors at moderate computational cost.

The calculation of binding free energies is important in many condensed matter problems. Although formally exact computational methods have the potential to complement, add to, and even compete with experimental approaches, they are difficult to use and extremely time consuming. We describe a Grid-based approach for the calculation of relative binding free energies, which we call Steered Thermodynamic Integration calculations using Molecular Dynamics (STIMD), and its application to Src homology 2 (SH2) protein cell signalling domains. We show that the time taken to compute free energy differences using thermodynamic integration can be significantly reduced: potentially from weeks or months to days of wall-clock time. To be able to perform such accelerated calculations requires the ability to both run concurrently and control in realtime several parallel simulations on a computational Grid. We describe how the RealityGrid computational steering system, in conjunction with a scalable classical MD code, can be used to dramatically reduce the time to achieve a result. This is necessary to improve the adoption of this technique and further allows more detailed investigations into the accuracy and precision of thermodynamic integration. Initial results for the Src SH2 system are presented and compared to a reported experimental value. Finally, we discuss the significance of our approach.

The aim of this study is to elucidate the pharmacological mechanism underlying the effects of Ginseng Radix et Rhizoma (ginseng) in heart failure (HF), providing a theoretical foundation for its clinical application. The potential mechanism of ginseng in the context of HF was investigated using systems pharmacology that combined network pharmacology, Gene Expression Omnibus (GEO) analysis, molecular docking, and experimental verification. Network pharmacology was employed to identify drug-disease targets. Core gene targets were subsequently subjected to enrichment analysis by integrating network pharmacology with GEO. Molecular docking was utilized to predict the binding affinities between identified targets and ginseng compounds. Furthermore, the therapeutic efficacy of ginseng was validated in an isoproterenol (ISO)-induced rat model of HF. The modulation of key signaling pathways by ginseng was confirmed through Western blot analysis. A total of 154 potential targets of ginseng in the treatment of HF were identified through network pharmacology analysis. The analysis of GSE71613 revealed that the PI3K-Akt pathway, reactive oxygen species, oxidative phosphorylation, MAPK signaling, and Ras signaling pathways are predominantly associated with patients with HF. By integrating the findings from network pharmacology and GEO analysis, ginsenoside Rg1 and ginsenoside Rb3 were identified as the potential components in ginseng, while FN1 and PRKAA2 were recognized as key targets involved in the PI3K-AKT and AMPK pathways, respectively. Molecular docking analysis revealed a strong affinity between the potential components and the identified core targets. In vivo experiments indicated that the extract of ginseng (EPG) significantly ameliorated ISO-induced cardiac dysfunction by improving cardiac parameters such as cardiac left ventricular internal systolic diameter, left ventricular end-diastolic volume, left ventricular end systolic volume, and left ventricular ejection fraction, while also reducing malondialdehyde production. In addition, EPG was found to enhance superoxide dismutase activity and ATP levels, while concurrently reducing the levels of interleukin (IL)-1β, IL-6, and TNF-α. The extract also reduced myocardial oxygen consumption, inflammatory cell infiltration, and the number of damaged myocardial fibers. Moreover, EPG was observed to upregulate the expression of p-PI3K, p-AKT, p-AMPK, and Bcl-2, while downregulating the expression of p-NFκB, TGF-β, and Bax. The therapeutic effects of ginseng on HF are primarily mediated through the PI3K-Akt and AMPK pathways. Ginsenoside Rg1 and ginsenoside Rb3 have been identified as potential therapeutic agents for HF.

About 10-15% of all breast cancers comprise triple-negative breast cancer (TNBC), defined as cancer cells that lack ER, PR, and HER2 protein receptors. Due to the absence of these receptors, treating TNBC using conventional chemotherapy is challenging and, therefore, requires the discovery of novel chemotherapeutic agents derived from natural sources. The current work was intended to study the potential phytochemicals of Ajwa dates (Phoenix dactylifera L.) with the predicted potential targets (namely, Akt and PI3K) to determine possible TNBC inhibitors. We harnessed network pharmacology, molecular docking, drug-likeness studies, Molecular Dynamics (MD) simulation, and binding free energy (MM-GBSA) calculation to get phytochemicals with potential effects against TNBC. Firstly, molecular docking was performed on 125 phytochemicals against the Akt and PI3K proteins utilizing PyRx. Then, the phytochemicals with the highest binding affinity (≤ -8.1 kcal/mol) were examined for in silico drug-likeness and toxicity profiles. Finally, phytochemicals with optimal druglikeness and toxicity profiles were studied by Molecular Dynamics (MD) simulation and binding free energy (MM-GBSA) to identify compounds that can form stable complexes. The results of the network pharmacology revealed that the Akt and PI3K proteins are potential targets of TNBC for the phytochemicals of Phoenix dactylifera L. used in this study. The outcomes of molecular docking displayed that among 125 phytochemicals, 42 of them (with a binding affinity ≤ -8.1 kcal/mol) have potentially inhibiting effects on both proteins PI3K and Akt expressed in TNBC. Then, the results of in silico drug-likeness identified seven phytochemicals with optimal pharmacokinetic profiles. Furthermore, toxicity studies showed that three phytochemicals (namely, Chrysoeriol, Daidzein, and Glycitein) did not cause any toxicities. Finally, the Molecular Dynamics (MD) simulation studies and binding free energy (MM-GBSA) verified that Daidzein stayed within the binding cavities of both proteins (Akt and PI3K) by establishing a stable protein-ligand complex during simulation. Taken together, the current work emphasizes the potential effects of Daidzein from Phoenix dactylifera L. against TNBC, and it can be further studied to establish it as a standard chemotherapy for TNBC.

Immunoproteasome subunit LMP7 is an important target for development of various novel therapeutic agents. In the present study, we examined the detailed binding structures and free energies for a promising series of non-covalent inhibitors interacting with LMP7 by carrying out homology modeling, molecular docking, molecular dynamics (MD) simulations, binding free energy calculations, and binding energy decompositions. The obtained protein-inhibitor binding structures and energetic results have revealed some interesting structural features of the non-covalent inhibitors binding with the LMP7 subunit and valuable insights into the factors affecting the activity of these non-covalent inhibitors. Based on the MD-simulated protein–ligand binding structures, the calculated binding free energies are in good agreement with the experimental activity data for all of the inhibitors examined, which suggests that the computational protocol and the obtained structural insights are reasonable. The obtained computational insights, along with the binding free energy calculation protocol tested in this study, are expected to be valuable for rational design of new, more potent non-covalent inhibitors of LMP7.

Background: Human African trypanosomiasis (HAT), caused by Trypanosoma brucei, is a neglected tropical disease with limited treatments, highlighting the pressing need for new drugs. Cell division cycle-2-related kinase 12 (CRK12), a pivotal protein involved in the cell cycle regulation of T. brucei, has emerged as a promising therapeutic target for HAT, yet effective CRK12 inhibitors remain lacking. Methods: An integrated strategy combining computational modeling, virtual screening, molecular dynamics (MD) simulations, and experimental validation was adopted to discover potential inhibitors against CRK12. By using the predicted and refined 3D structure of CRK12 from AlphaFold2 and MD simulation, over 1.5 million compounds were screened based on multiple-scale molecular docking, and 26 compounds were selected for evaluation of biological activity based on anti-T. brucei bioassays. Dose–response curves were generated for the most potent inhibitors, and the interaction mechanism between the top four compounds and CRK12 was explored by MD simulations and MM/GBSA binding free energy analysis. Results: Of the 26 compounds, six compounds demonstrated sub-micromolar to low-micromolar IC50 values (0.85–3.50 µM). The top four hits, F733-0072, F733-0407, L368-0556, and L439-0038, exhibited IC50 values of 1.11, 1.97, 0.85, and 1.66 µM, respectively. Binding free energy and energy decomposition analyses identified ILE335, VAL343, PHE430, ALA433, and LEU482 as hotspot residues for compound binding. Hydrogen bonding analysis demonstrated that these compounds can form stable hydrogen bonds with the hinge residue ALA433, ensuring their stable binding within the active site. Conclusions: This study establishes a robust and cost-effective pipeline for CRK12 inhibitor discovery, identifying several novel inhibitors demonstrating promising anti-HAT activity. The newly discovered scaffolds exhibit structural diversity distinct from known CRK12 inhibitors, providing valuable lead compounds for anti-trypanosomal drug development.

The selective estrogen receptor downregulators (SERDs) are the new emerging class of drugs that are used for the treatment of endocrine resistance breast cancer. Elacestrant (ELA) is a new SERD, currently it is in phase II clinical trial. To understand the ELA-ERα interactions, the molecular docking analysis has been carried out. The ELA molecule binds with the helices H3, H5, H6, and H11 and forms important intermolecular interactions. In addition to this, the tetrahydronapthalene and phenyl rings of ELA are forming T-shaped π···π interactions with the Phe404 and Trp383 residues. Further to understand the stability and flexibility of ELA molecule in the active site of wild and mutated L536S ERα, 100ns molecular dynamics (MD) simulation was performed for both complexes. Interestingly, the MD analysis of wild complex revealed an interaction between ELA and the Asn532 of H11, which is an essential interaction for the downregulation/degradation of ERα, whereas this interaction is not observed in the mutated complex. The drug binding mechanism and H12 dynamics have been elucidated from the analysis of hydrogen bonding interactions and the secondary structure analysis. To explore the binding affinity of ELA molecule, the binding free energy and normal mode analyses were carried out. The per residue decomposition analysis also performed, which shows the contribution of individual amino acids. The principal component analysis and residue interaction network analysis were used to identify the modifications and the interaction between the residues. From the results of different analysis, the inhibition mechanism and downregulation of ERα-ELA complex has been investigated. © 2019 Wiley Periodicals, Inc.

Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.