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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7559| Drug repurposing for SARS-COV-2: A molecular docking, molecular dynamics, machine learning, and ab initio study | |
| Jatin Kashyap Dibakar Datta | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| arXiv:2201.00287v1 | |
| https://arxiv.org/abs/2201.00287 | |
| A molecule of size 125nm has caused around 272 million infections and 5.3 million deaths worldwide. Those numbers are 50.4 million and 802,951 in the USA. It is called SARS-CoV-2, which causes a disease known as COVID-19. The high damaging cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for this disease. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. But so far, there has been no success in finding therapeutics candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. We have selected a group of ligands from the ZINC database based upon previously partially successful candidates, i.e., Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir. We have used the following robust framework to screen the ligands; Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis. In total, we have analyzed 242,000(ligands)*9(proteins)= 2.178 million unique protein binding site/ligand combinations. The proteins were selected based on recent studies evaluating potential inhibitor binding sites. Step-I had filtered that number down to 10 ligands/protein-based on molecular docking binding energy, which is further filtered down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands per protein were analyzed in Step-II with a molecular dynamics modeling-based RMSD filter of less than 1Å. It finally suggested three ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) attacking different binding sites of the same protein(7BV2), which were further analyzed in Step III to find the rationale behind higher ligand stability. | |
| Cornell University | |
| 02-01-2022 | |
| Preimpreso | |
| arxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| OTRAS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
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| Drug repurposing for SARS COV2_A molecular docking, molecular dynamics, machine learning, and ab initio study.pdf | 5.75 MB | Adobe PDF | Visualizar/Abrir |