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SARS-CoV-2 Omicron XBB.1.5 may be a cautionary variant by in silico study

View ORCID ProfileAki Sugano, Haruyuki Kataguchi, Mika Ohta, Yoshiaki Someya, View ORCID ProfileShigemi Kimura, Yoshimasa Maniwa, Toshihide Tabata, View ORCID ProfileYutaka Takaoka
doi: https://doi.org/10.1101/2023.01.18.524660
Aki Sugano
1Center for Clinical Research, Toyama University Hospital, Toyama 930-0194, Japan
2Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
3Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
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  • ORCID record for Aki Sugano
Haruyuki Kataguchi
2Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
4Laboratory for Biological Information Processing, Graduate School of Science and Engineering, University of Toyama, Toyama 930-0887, Japan
5Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
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Mika Ohta
2Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
3Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
5Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
7Life Science Institute, Kobe Tokiwa University, Kobe, Hyogo 653-0838, Japan
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Yoshiaki Someya
2Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
8Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, Toyama 930-0194, Japan
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Shigemi Kimura
3Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
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  • ORCID record for Shigemi Kimura
Yoshimasa Maniwa
3Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
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Toshihide Tabata
4Laboratory for Biological Information Processing, Graduate School of Science and Engineering, University of Toyama, Toyama 930-0887, Japan
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Yutaka Takaoka
2Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
3Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
5Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
6Center for Advanced Antibody Drug Development, University of Toyama, Toyama 930-0194, Japan
7Life Science Institute, Kobe Tokiwa University, Kobe, Hyogo 653-0838, Japan
8Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, Toyama 930-0194, Japan
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  • ORCID record for Yutaka Takaoka
  • For correspondence: ytakaoka@med.u-toyama.ac.jp
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ABSTRACT

In this research, we aimed to predict the relative risk of the recent new variants of SARS-CoV-2 as based on our previous research. First, we performed the molecular docking simulation analyses of the spike proteins with human angiotensin-converting enzyme 2 (ACE2) to understand the binding affinities to human cells of three new variants of SARS-CoV-2, Omicron BQ.1, XBB.1 and XBB.1.5 Then, three variants were subjected to determine the evolutionary distance of the spike protein gene (S gene) from the Wuhan, Omicron BA.1 and Omicron BA.4/5 variants, to appreciate the changes in the S gene. The result indicated that the XBB.1.5 had the highest binding affinity level of the spike protein with ACE2 and the longest evolutionary distance of the S gene. It suggested that the XBB.1.5 may be infected farther and faster than can infections of preexisting variants.

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations

    S gene
    spike protein gene
    ACE2
    angiotensin-converting enzyme
    RBD
    receptor binding domain
  • Copyright 
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    Posted January 19, 2023.
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    SARS-CoV-2 Omicron XBB.1.5 may be a cautionary variant by in silico study
    Aki Sugano, Haruyuki Kataguchi, Mika Ohta, Yoshiaki Someya, Shigemi Kimura, Yoshimasa Maniwa, Toshihide Tabata, Yutaka Takaoka
    bioRxiv 2023.01.18.524660; doi: https://doi.org/10.1101/2023.01.18.524660
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    SARS-CoV-2 Omicron XBB.1.5 may be a cautionary variant by in silico study
    Aki Sugano, Haruyuki Kataguchi, Mika Ohta, Yoshiaki Someya, Shigemi Kimura, Yoshimasa Maniwa, Toshihide Tabata, Yutaka Takaoka
    bioRxiv 2023.01.18.524660; doi: https://doi.org/10.1101/2023.01.18.524660

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