Anti-atherogenic effect of Nepitrin-7-O-glucoside: A flavonoid isolated from Nepeta hindostana via acting on PPAR – α receptor

Devi, Sushma; Rangra, Naresh Kumar; Rawat, Ravi; Alrobaian, Majed; Alam, Aftab; Singh, Randhir; Singh, Akanksha

doi:10.1016/j.steroids.2020.108770

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…The PME algorithm ( Di Pierro et al, 2015 ), integrated into GROMACS, was employed to minimize the error resulting from truncation. The trajectory files were visualized using Visual Molecular Dynamics (VMD) 1.9.2 ( Humphrey et al, 1996 ) and analyzed using the in-house developed tool HeroMD Analysis ( Devi et al, 2021 ; Rawat et al, 2021 ) and Xmgrace 5.1.25 ( Vaught, 1996 ).…”

Section: Methodsmentioning

confidence: 99%

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Siddique,

Anwaar,

Bashir

et al. 2024

Front. Chem.

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Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs.Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively.Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis.Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

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Section: Methodsmentioning

confidence: 99%

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Siddique,

Anwaar,

Bashir

et al. 2024

Front. Chem.

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“…The trajectory files are visualized through VMD (Visual Molecular Dynamics) 1.9.2. 24 and analyzed by indigenously developed tool HeroMDAnalysis 25 , 26 and Xmgrace 5.1.25. 27…”

Section: Methodsmentioning

confidence: 99%

Unraveling the therapeutic potential of quercetin and quercetin-3-O-glucuronide in Alzheimer's disease through network pharmacology, molecular docking, and dynamic simulations

Sabarathinam

2024

Sci Rep

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Quercetin is a flavonoid with notable pharmacological effects and promising therapeutic potential. Quercetin plays a significant role in neuroinflammation, which helps reduce Alzheimer's disease (AD) severity. Quercetin (Q) and quercetin 3-O-glucuronide (Q3OG) are some of the most potent antioxidants available from natural sources. However, the natural form of quercetin converted into Q3OG when reacted with intestinal microbes. The study aims to ensure the therapeutic potential of Q and Q3OG. In this study, potential molecular targets of Q and Q3OG were first identified using the Swiss Target Prediction platform and pathogenic targets of AD were identified using the DisGeNET database. Followed by compound and disease target overlapping, 77 targets were placed in that AKT1, EGFR, MMP9, TNF, PTGS2, MMP2, IGF1R, MCL1, MET and PARP1 was the top-ranked target, which was estimated by CytoHubba plug-in. The Molecular docking was performed for Q and Q3OG towards the PDB:1UNQ target. The binding score of Q and Q3OG was − 6.2 kcal/mol and − 6.58 kcal/mol respectively. Molecular dynamics simulation was conducted for Q and Q3OG towards the PDB:1UNQ target at 200 ns. This study's results help identify the multiple target sites for the bioactive compounds. Thus, synthesizing new chemical entity-based quercetin on structural modification may aid in eradicating AD complications.

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“…The trajectory data are analysed using VMD (Visual Molecular Dynamics) 1.9.2 [52] . and assessed using indigenously developed tool HeroMDAnalysis [53] and Xmgrace 5.1.25 [54]…”

Section: Materials and Proceduresmentioning

confidence: 99%

Unravelling the Design and Mechanism: Potential of Substituted Quinolines as PASTA subunit 4 Inhibitors for Antimicrobial Activity

Sharma,

Sanduja

et al. 2024

ChemistrySelect

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The increasing incidences of antibiotic‐resistance in Microorganisms have become a critical global health challenge. Traditional treatments, often relying on multiple antibiotics, are plagued by issues like drug resistance, reduced effectiveness, and heightened toxicity. In response, there is a compelling demand for innovative anti‐microbial agents that offer novel mechanisms of action. The family of bacterial Penicillin‐binding protein And Serine/Threonine kinase‐Associated (PASTA) kinases is of particular interest due to the role of these kinases in regulating resistance to ‐lactam antibiotics. As such, small molecule kinase inhibitors that target PASTA kinases may prove beneficial as treatments adjunctive to ‐lactam therapy. Our study was primarily geared towards identifying novel PASTA Kinase inhibitors through a ligand‐based drug design approach. Subsequently, we curated a library of 12 molecules, incorporating chemical modifications guided by considerations like drug‐like properties, chemical accessibility, and synthetic feasibility. Molecular docking analyses conducted on this library pinpointed three molecules with noteworthy binding affinities. From this subset, KS_QD_04 and KS_QD_05 emerged as promising candidates, subsequently validated through MD simulation studies, bolstering their potential as lead compounds in the quest to develop PASTA Kinase inhibitors for combating raising microbial resistance towards antibiotics.

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Anti-atherogenic effect of Nepitrin-7-O-glucoside: A flavonoid isolated from Nepeta hindostana via acting on PPAR – α receptor

Cited by 15 publications

References 7 publications

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Unraveling the therapeutic potential of quercetin and quercetin-3-O-glucuronide in Alzheimer's disease through network pharmacology, molecular docking, and dynamic simulations

Unravelling the Design and Mechanism: Potential of Substituted Quinolines as PASTA subunit 4 Inhibitors for Antimicrobial Activity

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