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A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions
Fast and accurate calculation of intermolecular interaction energies is desirable for understanding many chemical and biological processes, including the binding of small molecules to proteins. The Splinter [“Symmetry-adapted perturbation theory (SAPT0) protein-ligand interaction”] dataset has been...
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/PMC10497680/ https://www.ncbi.nlm.nih.gov/pubmed/37699937 http://dx.doi.org/10.1038/s41597-023-02443-1 |
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author | Spronk, Steven A. Glick, Zachary L. Metcalf, Derek P. Sherrill, C. David Cheney, Daniel L. |
author_facet | Spronk, Steven A. Glick, Zachary L. Metcalf, Derek P. Sherrill, C. David Cheney, Daniel L. |
author_sort | Spronk, Steven A. |
collection | PubMed |
description | Fast and accurate calculation of intermolecular interaction energies is desirable for understanding many chemical and biological processes, including the binding of small molecules to proteins. The Splinter [“Symmetry-adapted perturbation theory (SAPT0) protein-ligand interaction”] dataset has been created to facilitate the development and improvement of methods for performing such calculations. Molecular fragments representing commonly found substructures in proteins and small-molecule ligands were paired into >9000 unique dimers, assembled into numerous configurations using an approach designed to adequately cover the breadth of the dimers’ potential energy surfaces while enhancing sampling in favorable regions. ~1.5 million configurations of these dimers were randomly generated, and a structurally diverse subset of these were minimized to obtain an additional ~80 thousand local and global minima. For all >1.6 million configurations, SAPT0 calculations were performed with two basis sets to complete the dataset. It is expected that Splinter will be a useful benchmark dataset for training and testing various methods for the calculation of intermolecular interaction energies. |
format | Online Article Text |
id | pubmed-10497680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104976802023-09-14 A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions Spronk, Steven A. Glick, Zachary L. Metcalf, Derek P. Sherrill, C. David Cheney, Daniel L. Sci Data Data Descriptor Fast and accurate calculation of intermolecular interaction energies is desirable for understanding many chemical and biological processes, including the binding of small molecules to proteins. The Splinter [“Symmetry-adapted perturbation theory (SAPT0) protein-ligand interaction”] dataset has been created to facilitate the development and improvement of methods for performing such calculations. Molecular fragments representing commonly found substructures in proteins and small-molecule ligands were paired into >9000 unique dimers, assembled into numerous configurations using an approach designed to adequately cover the breadth of the dimers’ potential energy surfaces while enhancing sampling in favorable regions. ~1.5 million configurations of these dimers were randomly generated, and a structurally diverse subset of these were minimized to obtain an additional ~80 thousand local and global minima. For all >1.6 million configurations, SAPT0 calculations were performed with two basis sets to complete the dataset. It is expected that Splinter will be a useful benchmark dataset for training and testing various methods for the calculation of intermolecular interaction energies. Nature Publishing Group UK 2023-09-12 /pmc/articles/PMC10497680/ /pubmed/37699937 http://dx.doi.org/10.1038/s41597-023-02443-1 Text en © The Author(s) 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Spronk, Steven A. Glick, Zachary L. Metcalf, Derek P. Sherrill, C. David Cheney, Daniel L. A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
title | A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
title_full | A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
title_fullStr | A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
title_full_unstemmed | A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
title_short | A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
title_sort | quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497680/ https://www.ncbi.nlm.nih.gov/pubmed/37699937 http://dx.doi.org/10.1038/s41597-023-02443-1 |
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