Computational Approaches to Discover Novel Natural Compounds for SARS‐CoV‐2 Therapeutics

Rampogu, Shailima; Lee, Gihwan; Kulkarni, Apoorva; Kim, Dong Hwan; Yoon, Sanghwa; Kim, Myeong Ok; Lee, Keun Woo

doi:10.1002/open.202000332

Cited by 8 publications

(3 citation statements)

References 63 publications

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“…Correspondingly, in the current work, we targeted the protein that is essential in the proteolytic process, the M pro of SARS-CoV-2. Earlier, we have worked on M pro and nsp16 (2'- O –methyltransferase) to discover potential inhibitors against SARS-CoV-2 ( (53) , (54) , (55) ).…”

Section: Discussionmentioning

confidence: 99%

Unravelling the therapeutic potential of marine drugs as SARS-CoV-2 inhibitors: An insight from essential dynamics and free energy landscape

Rampogu

Gajula

Lee

et al. 2021

Computers in Biology and Medicine

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Coronavirus disease 2019 (COVID-19) is an ongoing pandemic. The virus that causes the disease, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), predominantly infects the respiratory tract, which may lead to pneumonia and death in severe cases. Many marine compounds have been found to have immense medicinal value and have gained approval from the Food and Drug Administration (FDA), and some are being tested in clinical trials. In the current investigation, we redirected a number of marine compounds toward SARS-CoV-2 by targeting the main protease (M pro , PDB ID: 6Y2F), subjecting them to several advanced computational techniques using co-crystallised ligand as the reference compound. The results of the binding affinity studies showed that two compounds, eribulin mesylate (eri) and soblidotin (sob), displayed higher docking scores than did the reference compound. When these compounds were assessed using molecular dynamics simulation, it was evident that they demonstrated stable binding at the binding pocket of the target protein. The systems demonstrated stable root mean square deviation and radius of gyration values, while occupying the binding pocket during the simulation run. Furthermore, the essential dynamics and free energy landscape exploration revealed that the protein had navigated through a minimal energy basin and demonstrated favourable conformation while binding to the proposed inhibitors. Collectively, our findings suggest that two marine compounds, namely eri and sob, show potential as SARS-CoV-2 main protease inhibitors.

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Section: Discussionmentioning

confidence: 99%

Unravelling the therapeutic potential of marine drugs as SARS-CoV-2 inhibitors: An insight from essential dynamics and free energy landscape

Rampogu

Gajula

Lee

et al. 2021

Computers in Biology and Medicine

Self Cite

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“…The study evaluated approximately 21,000 compounds extracted from three prominent, large-scale natural product databases globally: InterBioScreen [18 Fig. 2 The 3D structure of NEK7, integrated with ADP (cyan) (PDB ID: 2WQN).…”

Section: Preparation Of Ligandmentioning

confidence: 99%

In silico study to identify novel NEK7 inhibitors from natural source by a combination strategy

Zhang

Yao

et al. 2023

Preprint

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Cancer remains a significant health problem and stands as one of the primary causes of death worldwide. NEK7, a NIMA-related protein kinase, plays a crucial role in spindle assembly and cell division. Dysregulation of the NEK7 protein contributes to the development and progression of various malignancies, such as colon cancer and breast cancer. Therefore, the inhibition of NEK7 shows promise as a potential clinical target for anticancer therapy. Nevertheless, there is a dearth of high-quality NEK7 inhibitors. In this study, we utilized virtual screening, molecular docking, silicon-based pharmacokinetics, molecular dynamics (MD) simulations, and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA)-based binding free energy calculations to comprehensively analyze effective natural inhibitors that target NEK7 within the current framework. By employing molecular docking, including semi-flexible and flexible docking methods, we identified three natural products as hit compounds with binding modes similar to the active control dabrafenib. ADME/T predictions indicated that these hit molecules exhibited lower toxicity when administered orally. Additionally, through DFT calculations, we determined that the popular compound (-)-balanol possessed high chemical activity. Finally, 100 ns molecular dynamics simulations and energy decomposition revealed that the hit compounds displayed superior binding energy compared to the active control and demonstrated higher affinity. Based on the findings of our current research, we conclude that these newly discovered natural inhibitors may serve as parent structures for the development of more potent derivatives with promising biological activities. However, further experimental validation is necessary as part of subsequent investigations.

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“…Tatar et al 1140 aimed to repurpose 34 antiviral compounds to inhibit N protein. 1140 Following the workflow of ADMET screening, docking, optimizing of the top predictions by MD simulations, and redocking, Rampogu et al 1141 screened potential nsp16 inhibitors from 59619 natural compounds.…”

Section: Topics In Bioinformatics and Cheminformaticsmentioning

confidence: 99%

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus–host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein–protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

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Computational Approaches to Discover Novel Natural Compounds for SARS‐CoV‐2 Therapeutics

Cited by 8 publications

References 63 publications

Unravelling the therapeutic potential of marine drugs as SARS-CoV-2 inhibitors: An insight from essential dynamics and free energy landscape

Unravelling the therapeutic potential of marine drugs as SARS-CoV-2 inhibitors: An insight from essential dynamics and free energy landscape

In silico study to identify novel NEK7 inhibitors from natural source by a combination strategy

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

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