Loading...
Please wait, while we are loading the content...
Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease
| Content Provider | Scilit |
|---|---|
| Author | Joshi, Tanuja Joshi, Tushar Pundir, Hemlata Sharma, Priyanka Mathpal, Shalini Chandra, Subhash |
| Copyright Year | 2020 |
| Description | The whole world is facing a great challenging time due to Coronavirus disease (COVID-19) caused by SARS-CoV-2. Globally, more than 14.6 M people have been diagnosed and more than 595 K deaths are reported. Currently, no effective vaccine or drugs are available to combat COVID-19. Therefore, the whole world is looking for new drug candidates that can treat the COVID-19. In this study, we conducted a virtual screening of natural compounds using a deep-learning method. A deep-learning algorithm was used for the predictive modeling of a CHEMBL3927 dataset of inhibitors of Main protease (Mpro). Several predictive models were developed and evaluated based on $R^{2}$, MAE MSE, RMSE, and Loss. The best model with $R^{2}$=0.83, MAE = 1.06, MSE = 1.5, RMSE = 1.2, and loss = 1.5 was deployed on the Selleck database containing 1611 natural compounds for virtual screening. The model predicted 500 hits showing the value score between 6.9 and 3.8. The screened compounds were further enriched by molecular docking resulting in 39 compounds based on comparison with the reference (X77). Out of them, only four compounds were found to be drug-like and three were non-toxic. The complexes of compounds and Mpro were finally subjected to Molecular dynamic (MD) simulation for 100 ns. The MMPBSA result showed that two compounds Palmatine and Sauchinone formed very stable complex with Mpro and had free energy of −71.47 kJ $mol^{−1}$ and −71.68 kJ $mol^{−1}$ respectively as compared to X77 (−69.58 kJ $mol^{−1}$). From this study, we can suggest that the identified natural compounds may be considered for therapeutic development against the SARS-CoV-2. Communicated by Ramaswamy H. Sarma |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484589/pdf |
| Ending Page | 6746 |
| Page Count | 19 |
| Starting Page | 6728 |
| ISSN | 07391102 |
| e-ISSN | 15380254 |
| DOI | 10.1080/07391102.2020.1802341 |
| Journal | Journal of Biomolecular Structure and Dynamics |
| Issue Number | 17 |
| Volume Number | 39 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-11-22 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Biomolecular Structure and Dynamics Biology Mathematical and Computational Biology Covid-19 Deep Learning Molecular Docking Main Protease (mpro) Natural Compounds |
| Content Type | Text |
| Resource Type | Article |
| Subject | Structural Biology Medicine Molecular Biology |
