Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

View ORCID ProfileNatália Teruel, View ORCID ProfileOlivier Maihot, View ORCID ProfileRafael Josef Najmanovich
doi: https://doi.org/10.1101/2020.12.16.423118
Natália Teruel
1Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Natália Teruel
Olivier Maihot
1Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal
2Institute for Research in Immunology and Cancer (IRIC), Faculty of Medicine, Université de Montréal
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Olivier Maihot
Rafael Josef Najmanovich
1Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rafael Josef Najmanovich
  • For correspondence: rafael.najmanovich@umontreal.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

The COVID-19 pandemic has greatly affected us all, from individuals to the world economy. Whereas great advances have been achieved in record time, a lot remains to be learned about the infection mechanisms of its causative agent, the SARS-CoV-2 coronavirus. The Spike protein interacts with the human angiotensin converting enzyme 2 receptor as part of the viral entry mechanism. To do so, the receptor binding domain (RBD) of Spike needs to be in an open state conformation. Here we utilise coarse-grained normal mode analyses to model the dynamics of the SARS-CoV-2 Spike protein and the transition probabilities between open and closed conformations for the wild type, the D614G mutant as well other variants isolated experimentally. We proceed to perform several possible in silico single mutations of Spike, 17081 in total, to determine positions and specific Spike mutations that may affect the occupancy of the open and closed states. We estimate transition probabilities between the open and closed states from the calculated normal modes. Transition probabilities are employed in a Markov model to determine conformational state occupancies. Our results correctly model a shift in occupancy of the more infectious D614G strain towards higher occupancy of the open state via an increase of flexibility of the closed state and concomitant decrease of flexibility of the open state. Our results also suggest that the N501Y mutation recently observed, drastically increases the occupancy of the open state. We utilize global vibrational entropy differences to select candidate single point mutations that affect the flexibility of the open and closed states and confirm that these lead to shifts in occupancies for the most critical mutations. Among those, we observe a number of mutations on Glycine residues (404, 416, 504) and G252 in particular accepting a number of mutations. Other residues include K417, D467 and N501. This is, to our knowledge, the first use of normal mode analysis to model conformational state transitions and the effect of mutations thereon. The specific mutations of Spike identified here, while still requiring experimental validation, may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms as well as guide public health in their surveillance efforts.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/nataliateruel/data_Spike

  • https://github.com/gregorpatof/nrgten_package

Copyright 
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 4.0 International license.
Back to top
PreviousNext
Posted December 17, 2020.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants
Natália Teruel, Olivier Maihot, Rafael Josef Najmanovich
bioRxiv 2020.12.16.423118; doi: https://doi.org/10.1101/2020.12.16.423118
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants
Natália Teruel, Olivier Maihot, Rafael Josef Najmanovich
bioRxiv 2020.12.16.423118; doi: https://doi.org/10.1101/2020.12.16.423118

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biophysics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4680)
  • Biochemistry (10350)
  • Bioengineering (7670)
  • Bioinformatics (26325)
  • Biophysics (13521)
  • Cancer Biology (10682)
  • Cell Biology (15429)
  • Clinical Trials (138)
  • Developmental Biology (8496)
  • Ecology (12818)
  • Epidemiology (2067)
  • Evolutionary Biology (16847)
  • Genetics (11389)
  • Genomics (15474)
  • Immunology (10608)
  • Microbiology (25193)
  • Molecular Biology (10213)
  • Neuroscience (54447)
  • Paleontology (401)
  • Pathology (1668)
  • Pharmacology and Toxicology (2896)
  • Physiology (4341)
  • Plant Biology (9242)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2557)
  • Systems Biology (6777)
  • Zoology (1463)