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Takaoka Y, Sugano A, Morinaga Y, Ohta M, Miura K, Kataguchi H, Kumaoka M, Kimura S, Maniwa Y. Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2. MICROBIAL RISK ANALYSIS 2022; 22:100227. [PMID: 35756961 PMCID: PMC9212987 DOI: 10.1016/j.mran.2022.100227] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 05/13/2023]
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.
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Affiliation(s)
- Yutaka Takaoka
- Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
- Center for Advanced Antibody Drug Development, University of Toyama, Toyama 930-0194, Japan
- Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
- Life Science Institute, Kobe Tokiwa University, Kobe, Hyogo 653-0838, Japan
| | - Aki Sugano
- Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
- Center for Clinical Research, Toyama University Hospital, Toyama 930-0194, Japan
| | - Yoshitomo Morinaga
- Department of Microbiology, Toyama University Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
| | - Mika Ohta
- Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
- Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
- Life Science Institute, Kobe Tokiwa University, Kobe, Hyogo 653-0838, Japan
| | - Kenji Miura
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
| | - Haruyuki Kataguchi
- Department of Computational Drug Design and Mathematical Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama 930-0194, Japan
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
| | - Minoru Kumaoka
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, Toyama 930-0194, Japan
- Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
| | - Shigemi Kimura
- Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
| | - Yoshimasa Maniwa
- Department of Medical Systems, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
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Photodermatoses: what's new. Curr Opin Pediatr 2022; 34:374-380. [PMID: 35836395 DOI: 10.1097/mop.0000000000001155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The purpose of this review is to summarize and highlight the recent literature in photodermatoses. In the past year, there have been many developments in this heterogeneous group of conditions. RECENT FINDINGS This review is divided by photodermatoses type, which include idiopathic photodermatoses, photodermatoses secondary to exogenous agents, photodermatoses secondary to endogenous agents (the porphyrias), and genodermatoses. The idiopathic photodermatoses section focuses on case series and reports highlighting new disease presentations or further disease characterization and new treatment strategies for these disorders. The second section discusses a unique case and has a brief update on photoallergens. Clinical, diagnostic, and treatment updates for porphyrias are discussed in Section 3. For genodermatoses, we discuss complications and neoplastic risk of xeroderma pigmentosum and a few highlights from other rare disorders. Finally, we conclude with a brief overview of photoprotection updates, from assessing sun-damaged skin to the most effective photoprotective agents. SUMMARY Up-to-date information will help providers identify and manage this rare group of disorders.
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