Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2

Yutaka Takaoka; Aki Sugano; Yoshitomo Morinaga; Mika Ohta; Kenji Miura; Haruyuki Kataguchi; Minoru Kumaoka; Shigemi Kimura; Yoshimasa Maniwa

doi:10.1016/j.mran.2022.100227

Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2

Microb Risk Anal. 2022 Dec:22:100227. doi: 10.1016/j.mran.2022.100227. Epub 2022 Jun 16.

Authors

Yutaka Takaoka^{1

2

3

4

5}, Aki Sugano^{4

6}, Yoshitomo Morinaga⁷, Mika Ohta^{1

2

4

5}, Kenji Miura², Haruyuki Kataguchi^{1

2}, Minoru Kumaoka^{2

4}, Shigemi Kimura⁴, Yoshimasa Maniwa⁴

Affiliations

¹ Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan.
² Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan.
³ Center for Advanced Antibody Drug Development, University of Toyama, Toyama 930-0194, Japan.
⁴ Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan.
⁵ Life Science Institute, Kobe Tokiwa University, Kobe, Hyogo 653-0838, Japan.
⁶ Center for Clinical Research, Toyama University Hospital, Toyama 930-0194, Japan.
⁷ Department of Microbiology, Toyama University Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan.

Abstract

Objectives: Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2.

Methods: We subjected the six variants and the wild type of spike protein and human angiotensin-converting enzyme 2 (ACE2) to molecular docking simulation analyses to understand the binding affinity of spike protein and ACE2. We then utilized regression analysis of the correlation coefficient of the mathematical model and the infectivity of SARS-CoV-2 to predict infectivity.

Results: The evolutionary distance of the S gene correlated with the infectivity of SARS-CoV-2 variants. The calculated biding affinity for the mathematical model obtained with results of molecular docking simulation also correlated with the infectivity of SARS-CoV-2 variants. These results suggest that the data from the docking simulation for the receptor binding domain of variant spike proteins and human ACE2 were valuable for prediction of SARS-CoV-2 infectivity.

Conclusion: We developed a mathematical model for prediction of SARS-CoV-2 variant infectivity by using binding affinity obtained via molecular docking and the evolutionary distance of the S gene.

Keywords: Binding affinity; COVID-19; Infectivity; Mathematical model; SARS-CoV-2; Spike protein.