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SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures
Leveraging recent advances in computational modeling of proteins with AlphaFold2 (AF2) we provide a complete curated data set of all single mutations from each of the 7 main SARS-CoV-2 lineages spike protein receptor binding domain (RBD) resulting in 3819X7 = 26733 PDB structures. We visualize the g...
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/PMC10013278/ https://www.ncbi.nlm.nih.gov/pubmed/36918581 http://dx.doi.org/10.1038/s41597-023-02035-z |
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author | Kilim, Oz Mentes, Anikó Pál, Balázs Csabai, István Gellért, Ákos |
author_facet | Kilim, Oz Mentes, Anikó Pál, Balázs Csabai, István Gellért, Ákos |
author_sort | Kilim, Oz |
collection | PubMed |
description | Leveraging recent advances in computational modeling of proteins with AlphaFold2 (AF2) we provide a complete curated data set of all single mutations from each of the 7 main SARS-CoV-2 lineages spike protein receptor binding domain (RBD) resulting in 3819X7 = 26733 PDB structures. We visualize the generated structures and show that AF2 pLDDT values are correlated with state-of-the-art disorder approximations, implying some internal protein dynamics are also captured by the model. Joint increasing mutational coverage of both structural and phenotype data coupled with advances in machine learning can be leveraged to accelerate virology research, specifically future variant prediction. We hope this data release can offer assistance into further understanding of the local and global mutational landscape of SARS-CoV-2 as well as provide insight into the biological understanding that 3D structure acts as a bridge between protein genotype and phenotype. |
format | Online Article Text |
id | pubmed-10013278 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100132782023-03-14 SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures Kilim, Oz Mentes, Anikó Pál, Balázs Csabai, István Gellért, Ákos Sci Data Data Descriptor Leveraging recent advances in computational modeling of proteins with AlphaFold2 (AF2) we provide a complete curated data set of all single mutations from each of the 7 main SARS-CoV-2 lineages spike protein receptor binding domain (RBD) resulting in 3819X7 = 26733 PDB structures. We visualize the generated structures and show that AF2 pLDDT values are correlated with state-of-the-art disorder approximations, implying some internal protein dynamics are also captured by the model. Joint increasing mutational coverage of both structural and phenotype data coupled with advances in machine learning can be leveraged to accelerate virology research, specifically future variant prediction. We hope this data release can offer assistance into further understanding of the local and global mutational landscape of SARS-CoV-2 as well as provide insight into the biological understanding that 3D structure acts as a bridge between protein genotype and phenotype. Nature Publishing Group UK 2023-03-14 /pmc/articles/PMC10013278/ /pubmed/36918581 http://dx.doi.org/10.1038/s41597-023-02035-z 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 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 Kilim, Oz Mentes, Anikó Pál, Balázs Csabai, István Gellért, Ákos SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures |
title | SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures |
title_full | SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures |
title_fullStr | SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures |
title_full_unstemmed | SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures |
title_short | SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures |
title_sort | sars-cov-2 receptor-binding domain deep mutational alphafold2 structures |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013278/ https://www.ncbi.nlm.nih.gov/pubmed/36918581 http://dx.doi.org/10.1038/s41597-023-02035-z |
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