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Cited by 4SARS-CoV-2 and COVID-19 Researchvaccines and immunoinformatics approachesSARS-CoV-2 detection and testing

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Accurate predictions of SARS-CoV-2 infectivity from comprehensive analysis

Open access
Park, Jongkeun, Choi, WonJong, Seong, Do Young, Jeong, Seungpil, Lee, Ju Young, Park, Hyo Jeong, Chung, Dae Sun, Yi, Kijong, Kim, Uijin, Yoon, Ga-Yeon, Kim, Hyeran, Kim, Taehoon, Ko, Sooyeon, Min, Eun Jeong, Cho, Hyun-Soo, Cho, Nam-Hyuk, Hong, Dongwan·2024·eLife
DOI 10.7554/elife.99833.3PMC PMC11668528

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Abstract

An unprecedented amount of SARS-CoV-2 data has been accumulated compared with previous infectious diseases, enabling insights into its evolutionary process and more thorough analyses. This study investigates SARS-CoV-2 features as it evolved to evaluate its infectivity. We examined viral sequences and identified the polarity of amino acids in the receptor binding motif (RBM) region. We detected an increased frequency of amino acid substitutions to lysine (K) and arginine (R) in variants of concern (VOCs). As the virus evolved to Omicron, commonly occurring mutations became fixed components of the new viral sequence. Furthermore, at specific positions of VOCs, only one type of amino acid substitution and a notable absence of mutations at D467 were detected. We found that the binding affinity of SARS-CoV-2 lineages to the ACE2 receptor was impacted by amino acid substitutions. Based on our discoveries, we developed APESS, an evaluation model evaluating infectivity from biochemical and mutational properties. In silico evaluation using real-world sequences and in vitro viral entry assays validated the accuracy of APESS and our discoveries. Using Machine Learning, we predicted mutations that had the potential to become more prominent. We created AIVE, a web-based system, accessible at https://ai-ve.org to provide infectivity measurements of mutations entered by users. Ultimately, we established a clear link between specific viral properties and increased infectivity, enhancing our understanding of SARS-CoV-2 and enabling more accurate predictions of the virus.

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References 41 · Cited by 4

Research works printed but not linked: 40

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Metadata

Authors
Park, J., Choi, W., Seong, D. Y., Jeong, S., Lee, J. Y., Park, H. J., Chung, D. S., Yi, K., Kim, U., Yoon, G.-Y., Kim, H., Kim, T., Ko, S., Min, E. J., Cho, H.-S., Cho, N.-H., & Hong, D.
Affiliations
  • Catholic University of Korea (KR)
  • Department of Medical Informatics, College of Medicine, The Catholic University of Korea
  • Graduate School of Medical Science and Engineering, Korea Advanced Institute and Technology
  • Yonsei University (KR)
  • Department of Microbiology and Immunology, Seoul National University College of Medicine
  • Seoul National University (KR)
  • School of Chemical and Biological Engineering, Seoul National University
  • The Catholic University of Korea Catholic Medical Center (KR)
Corresponding author
Hong, Dongwan
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2024-12-24
Volume
13
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.99833.3
PMC
PMC11668528
OpenAlex
W4399706995
Cited by
4
References
41
Citations by year
2026: 1 · 2025: 3
Type
posted content
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation of Korea (NRF-2021M3H9A2097227, NRF-2022R1A2C3008162, RS-2023-00220840, NRF: 2021M3A9I2080490)
  • Korea Health Industry Development Institute (RS-2023-00265923, RS-2024-00406488)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
Keywords
SARS-CoV-2, receptor-binding motif, amino acid substitutions, variants of concern, Omicron variant, ACE2 receptor binding affinity, viral evolution, web-based prediction tool
MeSH terms
Machine Learning, Angiotensin-Converting Enzyme 2, COVID-19, SARS-CoV-2, Humans, Mutation, Protein Binding, Amino Acid Substitution, Virus Internalization, Spike Glycoprotein, Coronavirus

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