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CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method
The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405999/ https://www.ncbi.nlm.nih.gov/pubmed/36008961 http://dx.doi.org/10.3390/biom12081067 |
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author | Chen, Hongjun Hu, Xiaotian Hu, Yanshi Zhou, Jiawen Chen, Ming |
author_facet | Chen, Hongjun Hu, Xiaotian Hu, Yanshi Zhou, Jiawen Chen, Ming |
author_sort | Chen, Hongjun |
collection | PubMed |
description | The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale. |
format | Online Article Text |
id | pubmed-9405999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94059992022-08-26 CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method Chen, Hongjun Hu, Xiaotian Hu, Yanshi Zhou, Jiawen Chen, Ming Biomolecules Article The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale. MDPI 2022-08-02 /pmc/articles/PMC9405999/ /pubmed/36008961 http://dx.doi.org/10.3390/biom12081067 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Hongjun Hu, Xiaotian Hu, Yanshi Zhou, Jiawen Chen, Ming CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method |
title | CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method |
title_full | CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method |
title_fullStr | CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method |
title_full_unstemmed | CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method |
title_short | CoVM(2): Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method |
title_sort | covm(2): molecular biological data integration of sars-cov-2 proteins in a macro-to-micro method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405999/ https://www.ncbi.nlm.nih.gov/pubmed/36008961 http://dx.doi.org/10.3390/biom12081067 |
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