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Online biophysical predictions for SARS-CoV-2 proteins

View ORCID ProfileLuciano Kagami, View ORCID ProfileJoel Roca-Martínez, View ORCID ProfileJose Gavaldá-García, View ORCID ProfilePathmanaban Ramasamy, View ORCID ProfileK. Anton Feenstra, View ORCID ProfileWim Vranken
doi: https://doi.org/10.1101/2020.12.04.411744
Luciano Kagami
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium
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Joel Roca-Martínez
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium
2Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium
3VIB Structural Biology Research Centre, Pleinlaan 2, Brussels 1050, Belgium
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Jose Gavaldá-García
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium
2Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium
3VIB Structural Biology Research Centre, Pleinlaan 2, Brussels 1050, Belgium
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Pathmanaban Ramasamy
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium
2Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium
3VIB Structural Biology Research Centre, Pleinlaan 2, Brussels 1050, Belgium
4VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium
5Department of Biomolecular Medicine, Faculty of Health Sciences and Medicine, Ghent University, Ghent 9000, Belgium
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K. Anton Feenstra
6IBIVU – Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
7AIMMS – Amsterdam Institute for Molecules Medicines and Systems, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
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Wim Vranken
1Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium
2Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium
3VIB Structural Biology Research Centre, Pleinlaan 2, Brussels 1050, Belgium
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Abstract

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. We here present a website (http://sars2.bio2byte.be/) 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 and protein-protein interaction propensities. These predictions attempt to capture the ‘emergent’ properties of the proteins, so the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide an indication of the biophysical variation that is observed in homologous proteins, which give an indication of the limits of the functionally relevant biophysical behaviour of these proteins. With this website, we therefore hope to provide researchers with further clues on the behaviour of SARS-CoV-2 proteins.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added ORCID codes and fixed author names Small changes to text manuscript

  • https://bio2byte.be/sars2

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 07, 2020.
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Online biophysical predictions for SARS-CoV-2 proteins
Luciano Kagami, Joel Roca-Martínez, Jose Gavaldá-García, Pathmanaban Ramasamy, K. Anton Feenstra, Wim Vranken
bioRxiv 2020.12.04.411744; doi: https://doi.org/10.1101/2020.12.04.411744
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Online biophysical predictions for SARS-CoV-2 proteins
Luciano Kagami, Joel Roca-Martínez, Jose Gavaldá-García, Pathmanaban Ramasamy, K. Anton Feenstra, Wim Vranken
bioRxiv 2020.12.04.411744; doi: https://doi.org/10.1101/2020.12.04.411744

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