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Online biophysical predictions for SARS-CoV-2 proteins
BACKGROUND: The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062939/ https://www.ncbi.nlm.nih.gov/pubmed/33892639 http://dx.doi.org/10.1186/s12860-021-00362-w |
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author | Kagami, Luciano Roca-Martínez, Joel Gavaldá-García, Jose Ramasamy, Pathmanaban Feenstra, K. Anton Vranken, Wim F. |
author_facet | Kagami, Luciano Roca-Martínez, Joel Gavaldá-García, Jose Ramasamy, Pathmanaban Feenstra, K. Anton Vranken, Wim F. |
author_sort | Kagami, Luciano |
collection | PubMed |
description | BACKGROUND: The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. MAIN: We present a website (https://bio2byte.be/sars2/) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour. CONCLUSION: The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12860-021-00362-w. |
format | Online Article Text |
id | pubmed-8062939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80629392021-04-23 Online biophysical predictions for SARS-CoV-2 proteins Kagami, Luciano Roca-Martínez, Joel Gavaldá-García, Jose Ramasamy, Pathmanaban Feenstra, K. Anton Vranken, Wim F. BMC Mol Cell Biol Database BACKGROUND: The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. MAIN: We present a website (https://bio2byte.be/sars2/) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour. CONCLUSION: The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12860-021-00362-w. BioMed Central 2021-04-23 /pmc/articles/PMC8062939/ /pubmed/33892639 http://dx.doi.org/10.1186/s12860-021-00362-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Database Kagami, Luciano Roca-Martínez, Joel Gavaldá-García, Jose Ramasamy, Pathmanaban Feenstra, K. Anton Vranken, Wim F. Online biophysical predictions for SARS-CoV-2 proteins |
title | Online biophysical predictions for SARS-CoV-2 proteins |
title_full | Online biophysical predictions for SARS-CoV-2 proteins |
title_fullStr | Online biophysical predictions for SARS-CoV-2 proteins |
title_full_unstemmed | Online biophysical predictions for SARS-CoV-2 proteins |
title_short | Online biophysical predictions for SARS-CoV-2 proteins |
title_sort | online biophysical predictions for sars-cov-2 proteins |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062939/ https://www.ncbi.nlm.nih.gov/pubmed/33892639 http://dx.doi.org/10.1186/s12860-021-00362-w |
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