A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

  1. David E. Gordon
  2. Gwendolyn M. Jang
  3. Mehdi Bouhaddou
  4. Jiewei Xu
  5. Kirsten Obernier
  6. Kris M. White
  7. Matthew J. O’Meara
  8. Veronica V. Rezelj
  9. Jeffrey Z. Guo
  10. Danielle L. Swaney
  11. Tia A. Tummino
  12. Ruth Hüttenhain
  13. Robyn M. Kaake
  14. Alicia L. Richards
  15. Beril Tutuncuoglu
  16. Helene Foussard
  17. Jyoti Batra
  18. Kelsey Haas
  19. Maya Modak
  20. Minkyu Kim
  21. Paige Haas
  22. Benjamin J. Polacco
  23. Hannes Braberg
  24. Jacqueline M. Fabius
  25. Manon Eckhardt
  26. Margaret Soucheray
  27. Melanie J. Bennett
  28. Merve Cakir
  29. Michael J. McGregor
  30. Qiongyu Li
  31. Bjoern Meyer
  32. Ferdinand Roesch
  33. Thomas Vallet
  34. Alice Mac Kain
  35. Lisa Miorin
  36. Elena Moreno
  37. Zun Zar Chi Naing
  38. Yuan Zhou
  39. Shiming Peng
  40. Ying Shi
  41. Ziyang Zhang
  42. Wenqi Shen
  43. Ilsa T. Kirby
  44. James E. Melnyk
  45. John S. Chorba
  46. Kevin Lou
  47. Shizhong A. Dai
  48. Inigo Barrio-Hernandez
  49. Danish Memon
  50. Claudia Hernandez-Armenta
  51. Jiankun Lyu
  52. Christopher J. P. Mathy
  53. Tina Perica
  54. Kala Bharath Pilla
  55. Sai J. Ganesan
  56. Daniel J. Saltzberg
  57. Ramachandran Rakesh
  58. Xi Liu
  59. Sara B. Rosenthal
  60. Lorenzo Calviello
  61. Srivats Venkataramanan
  62. Jose Liboy-Lugo
  63. Yizhu Lin
  64. Xi-Ping Huang
  65. YongFeng Liu
  66. Stephanie A. Wankowicz
  67. Markus Bohn
  68. Maliheh Safari
  69. Fatima S. Ugur
  70. Cassandra Koh
  71. Nastaran Sadat Savar
  72. Quang Dinh Tran
  73. Djoshkun Shengjuler
  74. Sabrina J. Fletcher
  75. Michael C. O’Neal
  76. Yiming Cai
  77. Jason C. J. Chang
  78. David J. Broadhurst
  79. Saker Klippsten
  80. Phillip P. Sharp
  81. Nicole A. Wenzell
  82. Duygu Kuzuoglu-Ozturk
  83. Hao-Yuan Wang
  84. Raphael Trenker
  85. Janet M. Young
  86. Devin A. Cavero
  87. Joseph Hiatt
  88. Theodore L. Roth
  89. Ujjwal Rathore
  90. Advait Subramanian
  91. Julia Noack
  92. Mathieu Hubert
  93. Robert M. Stroud
  94. Alan D. Frankel
  95. Oren S. Rosenberg
  96. Kliment A. Verba
  97. David A. Agard
  98. Melanie Ott
  99. Michael Emerman
  100. Natalia Jura
  101. Mark von Zastrow
  102. Eric Verdin
  103. Alan Ashworth
  104. Olivier Schwartz
  105. Christophe d’Enfert
  106. Shaeri Mukherjee
  107. Matt Jacobson
  108. Harmit S. Malik
  109. Danica G. Fujimori
  110. Trey Ideker
  111. Charles S. Craik
  112. Stephen N. Floor
  113. James S. Fraser
  114. John D. Gross
  115. Andrej Sali
  116. Bryan L. Roth
  117. Davide Ruggero
  118. Jack Taunton
  119. Tanja Kortemme
  120. Pedro Beltrao
  121. Marco Vignuzzi
  122. Adolfo García-Sastre
  123. Kevan M. Shokat
  124. Brian K. Shoichet
  125. Nevan J. Krogan

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.22.002386: (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

    Experimental Models: Cell Lines
    SentencesResources
    While the cells used for these AP-MS experiments, HEK-293T kidney cells, are permissive to SARS-CoV-2 infection31, they do not represent the primary physiological site of infection.
    HEK-293T
    suggested: None
    These data support the hypothesis that SARS-CoV-2 preferentially hijacks proteins available in lung tissue and the degree of tissue specific expression may facilitate the drug targeting of host factors with fewer systemic side-effects; this observation is particularly compelling given its derivation from HEK293 cells, which are themselves not lung derived.
    HEK293
    suggested: CLS Cat# 300192/p777_HEK293, CVCL_0045
    For each affinity purification, ten million HEK293T cells were plated per 15-cm dish and transfected with up to 15 μg of individual Strep-tagged expression constructs after 20-24 hours.
    HEK293T
    suggested: None
    Software and Algorithms
    SentencesResources
    However, they differ in their complement of 3’ open reading frames: SARS-CoV-2 possesses an Orf3b and Orf10 with limited detectable protein homology to SARS-CoV16, and its Orf8 is intact while SARS-CoV encodes Orf8a and Orf8b (Fig. 1b)1,16,18.
    SARS-CoV-2
    suggested: (Sino Biological Cat# 40143-R019, AB_2827973)
    In accordance to this, when compared to overall RefSeq gene expression in the lung (median=3.198 TPM), the interacting proteins were more highly expressed (median=25.52 TPM, p=0.0007; t-test, Extended Data Fig. 4) and were also specifically enriched in lung expression relative to other tissues (Extended Data Fig. 5).
    RefSeq
    suggested: (RefSeq, SCR_003496)
    First, a 75 min acquisition, in which peptides were directly injected via a Easy-nLC 1200 (Thermo) into a Q-Exactive Plus mass spectrometer (Thermo), with all MS1 and MS2 spectra collected in the orbitrap.
    Thermo
    suggested: (Thermo Xcalibur, SCR_014593)
    Detected peptides and proteins were filtered to 1% false discovery rate in MaxQuant, and identified proteins were then subjected to protein-protein interaction scoring with both SAINTexpress20 and MiST19,97.
    MaxQuant
    suggested: (MaxQuant, SCR_014485)
          <div style="margin-bottom:8px">
        <div><b>SAINTexpress20</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">All mass spectrometry raw data and search results files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD018117101,102.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>PRIDE</b></div>
        <div>suggested: (Pride-asap, <a href="https://scicrunch.org/resources/Any/search?q=SCR_012052">SCR_012052</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The over-representation analysis (ORA) was performed using the enricher function of clusterProfiler package in R with default parameters.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>clusterProfiler</b></div>
        <div>suggested: (clusterProfiler, <a href="https://scicrunch.org/resources/Any/search?q=SCR_016884">SCR_016884</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The model was visualized in PyMOL (The PyMOL Molecular Graphics System, Version 2.3.4 Schrödinger, LLC.).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>PyMOL</b></div>
        <div>suggested: (PyMOL, <a href="https://scicrunch.org/resources/Any/search?q=SCR_000305">SCR_000305</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">Protein E was structurally aligned to the histone subunits using Pymol’s “align” function (https://pymolwiki.org/index.php/Align).</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>Pymol’s</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">For both approaches, molecules were prioritized on their FDA approval status, activity at the target of interest better than 1 μM, and commercial availability, drawing on the ZINC database108.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>ZINC</b></div>
        <div>suggested: (Zinc, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008596">SCR_008596</a>)</div>
      </div>
    </td></tr></table>

    Results from OddPub: Thank you for sharing your data.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  2. Version published to 10.1038/s41586-020-2286-9
  4. Version published to 10.1101/2020.03.22.002386v3 on bioRxiv
  5. Version published to 10.1101/2020.03.22.002386v2 on bioRxiv
  6. Version published to 10.1101/2020.03.22.002386v1 on bioRxiv