Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculations

Pandya, Pujan; Kumar, Sivakumar Prasanth; Bhadresha, Kinjal; Patel, Chirag; Patel, Saumya; Rawal, Rakesh; Mankad, Archana

doi:10.1080/08927022.2020.1764552

Cited by 13 publications

(11 citation statements)

References 64 publications

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“…Following a thorough investigation, two FDA-approved molecules, ABBV744 and Onalespibas, have been recently recommended as possible inhibitors of SARSCoV2 main protease [ 34 ]. Computational methods have proven to be extremely useful in a number of other areas of life science [ [35] , [36] , [37] ]. Molecular docking and MD simulations are the central strategies for the in silico analysis in this approach, and many lead compounds are recently revealed to have the potential to influence the biochemistry and life cycle of SARS-CoV2.…”

Section: Discussionmentioning

confidence: 99%

Sterenin M as a potential inhibitor of SARS-CoV-2 main protease identified from MeFSAT database using molecular docking, molecular dynamics simulation and binding free energy calculation

Prajapati

Patel

Goswami

et al. 2021

Computers in Biology and Medicine

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The disease outbreak of Coronavirus disease-19 (COVID-19), caused by the novel SARS-CoV-2 virus, remains a public health concern. COVID-19 is spreading rapidly with a high mortality rate due to unavailability of effective treatment or vaccine for the disease. The high rate of mutation and recombination in SARS-CoV2 makes it difficult for scientist to develop specific anti-CoV2 drugs and vaccines. SARS-CoV-2-Mpro cleaves the viral polyprotein to produce a variety of non-structural proteins, but in human host it also cleaves the nuclear transcription factor kappa B (NF-κB) essential modulator (NEMO), which suppresses the activation of the NF-κB pathway and weakens the immune response. Since the main protease (Mpro) is required for viral gene expression and replication, it is a promising target for antagonists to treat novel coronavirus disease and discovery of high resolution crystal structure of SARS-CoV-2-Mpro provide an opportunity for in silico identification of its possible inhibitors. In this study we intend to find novel and potential Mpro inhibitors from around 1830 chemically diverse and therapeutically important secondary metabolites available in the MeFSAT database by performing molecular docking against the Mpro structure of SARS-CoV-2 (PDB ID: 6LZE). After ADMET (absorption, distribution, metabolism, excretion, and toxicity) profile and binding energy calculation through MM-GBSA for top five hits, Sterenin M was proposed as a SARS-CoV2-Mpro inhibitor with validation of molecular dynamics (MD) simulation study. Sterenin M seems to have the potential to be a promising ligand against SARS-CoV-2, and thus it requires further validation by in vitro and in vivo studies.

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Section: Discussionmentioning

confidence: 99%

Sterenin M as a potential inhibitor of SARS-CoV-2 main protease identified from MeFSAT database using molecular docking, molecular dynamics simulation and binding free energy calculation

Prajapati

Patel

Goswami

et al. 2021

Computers in Biology and Medicine

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“…We adopted the procedure previously used by Okpo et al (2001). Thirty mice were divided randomly into 5 groups containg six animals in each group (n = 6).…”

Section: Acetic Acid-induced Writhing Testmentioning

confidence: 99%

“…Also, Chagas-Paula et al (2015) predicted the anti-inflammatory activity of natural compounds from Asteraceae species using an artificial neural network (ANN) model. Furthermore, Pandya et al (2020), in their study, reported the use of computational approaches to predict the antiinflammatory action of bioactive compounds from Murraya koenigii and their pharmacological effects on COX-1 and COX-2.…”

Section: Introductionmentioning

confidence: 99%

The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach

et al. 2021

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Background Medicinal plants are used to manage pain and inflammatory disorders in traditional medicine. A scientific investigation could serve as a basis for the determination of molecular mechanisms of antinociceptive and antiinflammatory actions of herbal products. In this work, we used both artificial intelligence (AI) based models inform of adaptive neuro-fuzzy inference system and artificial neural network (ANN) as well as a linear model, namely; stepwise linear regression in modelling the performance of four different inflammatory biomarkers namely; interleukin (1L)-1β, 1L-6, tumour necrosis factor (TNF)-α and prostaglandin E2 (PGE2). This modelling was done using number of abdominal writes, the reaction time of paw licking in mice and paw oedema diameter as the input variables. Results Four different performance indices were employed, which are determination coefficient (DC), root mean squared error (RMSE), mean square error (MSE) and correlation co-efficient (CC). The results have shown the superiority of the AI-based models over the linear model. Conclusions The overall quantitative and visualized comparison of the results showed that adaptive neuro-fuzzy inference system outperformed the ANN and SWLR models in modelling the performance of the four inflammation biomarkers in both the calibration and verification phases.

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“…The results of molecular docking are dependent on two factors: optimization search method, which detects docking complexes with minimum binding energies, and scoring function is used as benchmark to evaluate results obtained after docking. [35]…”

Section: Molecular Dockingmentioning

confidence: 99%

“…Different trajectories were analyzed through several quantities, including root mean squared deviation (RMSD). [35]…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Analyzing the role of phytochemicals in targeting drug transporter protein ABCC6 using molecular docking and molecular dynamics simulations

Pandya

Patel

Bhavsar

et al. 2020

Int. J. Pharm. Sci. Drug Res.

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Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype that lacks hormonal receptors. This reduces the therapeutic options for TNBC patients creating more focus on chemotherapy. Drug resistance has posed as a major hurdle in treating TNBC patients. Deregulation of drug transporter proteins is one of major factors that cause resistance to chemotherapeutic drugs. In this study, ABCC6 a drug transporter protein that is found dysregulated in several resistant cancer cells has been docked with natural compounds or phytochemicals with known anti-cancer activities. Subtrifloralactone G, a withanolide extracted from Deprea subtriflora is found to show highest binding energy with ABCC6 protein. Molecular dynamics simulations further prove the stability of the ABCC6 protein- Subtrifloralactone G ligand complex. ADMET analysis shows that phytochemical Subtrifloralactone G can be used as an anti-cancer therapeutic drug in treating resistant cancer cells. The study mainly focuses on the role of phytochemicals in treating resistant TNBC cells.

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Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculations

Cited by 13 publications

References 64 publications

Sterenin M as a potential inhibitor of SARS-CoV-2 main protease identified from MeFSAT database using molecular docking, molecular dynamics simulation and binding free energy calculation

Sterenin M as a potential inhibitor of SARS-CoV-2 main protease identified from MeFSAT database using molecular docking, molecular dynamics simulation and binding free energy calculation

The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach

Analyzing the role of phytochemicals in targeting drug transporter protein ABCC6 using molecular docking and molecular dynamics simulations

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