Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic

  1. Joseph H. Lubin
  2. Christine Zardecki
  3. Elliott M. Dolan
  4. Changpeng Lu
  5. Zhuofan Shen
  6. Shuchismita Dutta
  7. John D. Westbrook
  8. Brian P. Hudson
  9. David S. Goodsell
  10. Jonathan K. Williams
  11. Maria Voigt
  12. Vidur Sarma
  13. Lingjun Xie
  14. Thejasvi Venkatachalam
  15. Steven Arnold
  16. Luz Helena Alfaro Alvarado
  17. Kevin Catalfano
  18. Aaliyah Khan
  19. Erika McCarthy
  20. Sophia Staggers
  21. Brea Tinsley
  22. Alan Trudeau
  23. Jitendra Singh
  24. Lindsey Whitmore
  25. Helen Zheng
  26. Matthew Benedek
  27. Jenna Currier
  28. Mark Dresel
  29. Ashish Duvvuru
  30. Britney Dyszel
  31. Emily Fingar
  32. Elizabeth M. Hennen
  33. Michael Kirsch
  34. Ali A. Khan
  35. Charlotte Labrie-Cleary
  36. Stephanie Laporte
  37. Evan Lenkeit
  38. Kailey Martin
  39. Marilyn Orellana
  40. Melanie Ortiz-Alvarez de la Campa
  41. Isaac Paredes
  42. Baleigh Wheeler
  43. Allison Rupert
  44. Andrew Sam
  45. Katherine See
  46. Santiago Soto Zapata
  47. Paul A. Craig
  48. Bonnie L. Hall
  49. Jennifer Jiang
  50. Julia R. Koeppe
  51. Stephen A. Mills
  52. Michael J. Pikaart
  53. Rebecca Roberts
  54. Yana Bromberg
  55. J. Steen Hoyer
  56. Siobain Duffy
  57. Jay Tischfield
  58. Francesc X. Ruiz
  59. Eddy Arnold
  60. Jean Baum
  61. Jesse Sandberg
  62. Grace Brannigan
  63. Sagar D. Khare
  64. Stephen K. Burley

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

Article activity feed

  1. Version published to 10.1101/2020.12.01.406637v3 on bioRxiv
  2. Version published to 10.1101/2020.12.01.406637v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.12.01.406637: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The structural model was then subjected to 10,000 steps of energy minimization in vacuum using NAMD 2.13 (Phillips et al., 2020) and the CHARMM 36 force field (MacKerell Jr. et al., 1998).
    NAMD
    suggested: (NAMD, RRID:SCR_014894)
    Ribbon/atomic stick figure representation figures were generated using Mol* and PyMOL (DeLano, 2002).
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    Rosetta-based Analyses of Substitution Location(s), Conservation, and Energetics: PyRosetta (Chaudhury, Lyskov, & Gray, 2010) was used to analyze each study protein and its observed USVs.
    Conservation
    suggested: (Conservation, RRID:SCR_016064)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    For each calculation type, the mean destabilization calculated for the core substitution distribution is smaller than the mean value associated with the second Gaussian peak observed in the full set of substitutions, possibly due to contributions to the second peak from destabilizing boundary layer substitutions that shift the mean to higher values (and possibly to limitations of the sampling and scoring approach discussed above). Bi-Gaussian fits to ΔΔGApp distributions for each of the 29 study proteins considered individually (Supplementary Table Gaussian) show similarly good fits for bi-Gaussian functions for globular study proteins. Robustness with respect to destabilizing effects of amino acid changes both limits and promotes viral evolution. It is, therefore, remarkable that the observed variation in the SARS-CoV-2 proteome over the first six months of the pandemic follows this universal trend, speaking perhaps to the relative rapidity of viral evolution due to large population sizes and imperfect replication machinery. Analyses of Study Proteins: The sections that follow provide more detailed results and discussion pertaining to USVs identified for 13 of the 29 SARS-CoV-2 study proteins, including one validated drug target [RNA-dependent RNA polymerase (RdRp, nsp7/nsp82/nsp12 heterotetramer)], five potential small-molecule drug discovery targets [papain-like proteinase (PLPro, part of nsp3), main protease (nsp5), RNA helicase (nsp13), proofreading exoribonuclease (nsp1...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.12.01.406637v1 on bioRxiv