Computational investigation of benzalacetophenone derivatives against SARS-CoV-2 as potential multi-target bioactive compounds

Khanal, Pukar; Patil, Vishal S.; Bhandare, Vishwambhar V.; Dwivedi, Prarambh S. R.; Shastry, C.S.; Patil, B. M.; Gurav, Shailendra; Harish, Darasaguppe R.; Roy, Subarna

doi:10.1016/j.compbiomed.2022.105668

Cited by 26 publications

(10 citation statements)

References 68 publications

(70 reference statements)

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“…The relative binding free energy and its contribution to individual residues were calculated using the MM-PBSA method and the “g_mmpbsa” tool ( Kumari et al, 2014 ; Dwivedi et al, 2022 ). The parameters for binding free energy calculations were taken from our previous study ( Khanal et al, 2022 ). The binding free energy (ΔG) was calculated using 50 representative snapshots taken throughout the stable trajectory observed between 100 and 150 ns.…”

Section: Methodsmentioning

confidence: 99%

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Khanal

Patil²,

Bhandare

et al. 2023

Front. Pharmacol.

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Aim: The purpose of this study was to establish a mode of action for diosgenin against breast cancer employing a range of system biology tools and to corroborate its results with experimental facts.Methodology: The diosgenin-regulated domains implicated in breast cancer were enriched in the Kyoto Encyclopedia of Genes and Genomes database to establish diosgenin-protein(s)-pathway(s) associations. Later, molecular docking and the lead complexes were considered for molecular dynamics simulations, MMPBSA, principal component, and dynamics cross-correlation matrix analysis using GROMACS v2021. Furthermore, survival analysis was carried out for the diosgenin-regulated proteins that were anticipated to be involved in breast cancer. For gene expression analyses, the top three targets with the highest binding affinity for diosgenin and tumor expression were examined. Furthermore, the effect of diosgenin on cell proliferation, cytotoxicity, and the partial Warburg effect was tested to validate the computational findings using functional outputs of the lead targets.Results: The protein-protein interaction had 57 edges, an average node degree of 5.43, and a p-value of 3.83e-14. Furthermore, enrichment analysis showed 36 KEGG pathways, 12 cellular components, 27 molecular functions, and 307 biological processes. In network analysis, three hub proteins were notably modulated: IGF1R, MDM2, and SRC, diosgenin with the highest binding affinity with IGF1R (binding energy −8.6 kcal/mol). Furthermore, during the 150 ns molecular dynamics (MD) projection run, diosgenin exhibited robust intermolecular interactions and had the least free binding energy with IGF1R (−35.143 kcal/mol) compared to MDM2 (−34.619 kcal/mol), and SRC (-17.944 kcal/mol). Diosgenin exhibited the highest cytotoxicity against MCF7 cell lines (IC50 12.05 ± 1.33) µg/ml. Furthermore, in H2O2-induced oxidative stress, the inhibitory constant (IC50 7.68 ± 0.51) µg/ml of diosgenin was lowest in MCF7 cell lines. However, the reversal of the Warburg effect by diosgenin seemed to be maximum in non-cancer Vero cell lines (EC50 15.27 ± 0.95) µg/ml compared to the rest. Furthermore, diosgenin inhibited cell proliferation in SKBR3 cell lines more though.Conclusion: The current study demonstrated that diosgenin impacts a series of signaling pathways, involved in the advancement of breast cancer, including FoxO, PI3K-Akt, p53, Ras, and MAPK signaling. Additionally, diosgenin established a persistent diosgenin-protein complex and had a significant binding affinity towards IGF1R, MDM2, and SRC. It is possible that this slowed down cell growth, countered the Warburg phenomenon, and showed the cytotoxicity towards breast cancer cells.

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Section: Methodsmentioning

confidence: 99%

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Khanal

Patil²,

Bhandare

et al. 2023

Front. Pharmacol.

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“…Using the “g_covar” and “g_anaeig” tools, which are already included in the GROMACS software package, the PCA was performed by first creating the covariance matrix of the Cα-atoms of the protein and then diagonalizing it. The xmgrace tool was used to plot the graphs. − …”

Section: Methodsmentioning

confidence: 99%

Design and Computational Analysis of an MMP9 Inhibitor in Hypoxia-Induced Glioblastoma Multiforme

Kumari

Kumar

2023

ACS Omega

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The main therapeutic difficulties in treating hypoxia-induced glioblastoma multiforme (GBM) are toxicity of current treatments and the resistance brought on by the microenvironment. More effective therapeutic alternatives are urgently needed to reduce tumor lethality. Hence, we screened plant-based natural product panels intending to identify novel drugs without elevating drug resistance. We explored GEO for the hypoxia GBM model and compared hypoxic genes to non-neoplastic brain cells. A total of 2429 differentially expressed genes expressed exclusively in hypoxia were identified. The functional enrichment analysis demonstrated genes associated with GBM, further PPI network was constructed, and biological pathways associated with them were explored. Seven webtools, including GEPIA2.0, TIMER2.0, TCGA-GBM, and GlioVis, were used to validate 32 hub genes discovered using Cytoscape tool in GBM patient samples. Four GBM-specific hypoxic hub genes, LYN, MMP9, PSMB9, and TIMP1, were connected to the tumor microenvironment using TIMER analysis. 11 promising hits demonstrated positive drug-likeness with nontoxic characteristics and successfully crossed blood−brain barrier and ADMET analyses. Top-ranking hits have stable intermolecular interactions with the MMP9 protein according to molecular docking, MD simulation, MM-PBSA, PCA, and DCCM analyses. Herein, we have reported flavonoids, 7,4′-dihydroxyflavan, (3R)-3-(4-hydroxybenzyl)-6-hydroxy-8-methoxy-3,4-dihydro-2H-1-benzopyran, and 4′-hydroxy-7methoxyflavan, to inhibit MMP9, a novel hypoxia gene signature that could serve as a promising predictor in various clinical applications, including GBM diagnosis, prognosis, and targeted therapy.

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“…We used the “amber-mmpbsa module” tool to perform binding energy calculations. The stable trajectory observed between the 90–100 ns was chosen for the binding energy calculation by selecting representative snapshots with an interval of 50 frames 28 .…”

Section: Methods and Analysismentioning

confidence: 99%

Exploring the mechanism of active components from ginseng to manage diabetes mellitus based on network pharmacology and molecular docking

Jin

et al. 2023

Sci Rep

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A large body of literature has shown that ginseng had a role in diabetes mellitus management. Ginsenosides are the main active components of ginseng. But what ginsenosides can manage in diabetic are not systematic. The targets of these ginsenosides are still incomplete. Our aim was to identify which ginsenosides can manage diabetes mellitus through network pharmacology and molecular docking. To identify the targets of these ginsenosides. In this work, we retrieved and screened ginsenosides and corresponding diabetes mellitus targets across multiple databases. PPI networks of the genes were constructed using STRING, and the core targets were screened out through topological analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed by using the R language. Finally, molecular docking was performed after bioinformatics analysis for verification. Our research results showed that 28 ginsenosides in ginseng might be against diabetes mellitus by modulating related proteins such as VEGFA, Caspase 3, and TNF-α. Among the 28 ginsenosides, 20(R)-Protopanaxatriol, 20(R)-Protopanaxadiol, and Ginsenoside Rg1 might play a significant role. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis showed that the management of diabetes mellitus by ginsenosides may be related to the positive regulation of reactive oxygen metabolic processes, associated with the insulin signaling pathway, TNF signaling pathway, and AMPK signaling pathway. Molecular docking results and molecular dynamics simulation showed that most ginsenosides could stably bind to the core target, mainly hydrogen bonding and hydrophobic bond. This study suggests the management of ginseng on diabetes mellitus. We believe that our results can contribute to the systematic study of the mechanism of ginsenosides for the management of diabetes mellitus. At the same time, it can provide a theoretical basis for subsequent studies on the management of ginsenosides in diabetes mellitus.

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Computational investigation of benzalacetophenone derivatives against SARS-CoV-2 as potential multi-target bioactive compounds

Cited by 26 publications

References 68 publications

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Design and Computational Analysis of an MMP9 Inhibitor in Hypoxia-Induced Glioblastoma Multiforme

Exploring the mechanism of active components from ginseng to manage diabetes mellitus based on network pharmacology and molecular docking

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