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SARS-CoV2 billion-compound docking
This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomp...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044124/ https://www.ncbi.nlm.nih.gov/pubmed/36977690 http://dx.doi.org/10.1038/s41597-023-01984-9 |
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author | Rogers, David M. Agarwal, Rupesh Vermaas, Josh V. Smith, Micholas Dean Rajeshwar, Rajitha T. Cooper, Connor Sedova, Ada Boehm, Swen Baker, Matthew Glaser, Jens Smith, Jeremy C. |
author_facet | Rogers, David M. Agarwal, Rupesh Vermaas, Josh V. Smith, Micholas Dean Rajeshwar, Rajitha T. Cooper, Connor Sedova, Ada Boehm, Swen Baker, Matthew Glaser, Jens Smith, Jeremy C. |
author_sort | Rogers, David M. |
collection | PubMed |
description | This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens. |
format | Online Article Text |
id | pubmed-10044124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100441242023-03-28 SARS-CoV2 billion-compound docking Rogers, David M. Agarwal, Rupesh Vermaas, Josh V. Smith, Micholas Dean Rajeshwar, Rajitha T. Cooper, Connor Sedova, Ada Boehm, Swen Baker, Matthew Glaser, Jens Smith, Jeremy C. Sci Data Data Descriptor This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens. Nature Publishing Group UK 2023-03-28 /pmc/articles/PMC10044124/ /pubmed/36977690 http://dx.doi.org/10.1038/s41597-023-01984-9 Text en © UT-Battelle, LLC 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Rogers, David M. Agarwal, Rupesh Vermaas, Josh V. Smith, Micholas Dean Rajeshwar, Rajitha T. Cooper, Connor Sedova, Ada Boehm, Swen Baker, Matthew Glaser, Jens Smith, Jeremy C. SARS-CoV2 billion-compound docking |
title | SARS-CoV2 billion-compound docking |
title_full | SARS-CoV2 billion-compound docking |
title_fullStr | SARS-CoV2 billion-compound docking |
title_full_unstemmed | SARS-CoV2 billion-compound docking |
title_short | SARS-CoV2 billion-compound docking |
title_sort | sars-cov2 billion-compound docking |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044124/ https://www.ncbi.nlm.nih.gov/pubmed/36977690 http://dx.doi.org/10.1038/s41597-023-01984-9 |
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