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Prediction and Evolution of the Molecular Fitness of SARS-CoV-2 Variants: Introducing SpikePro.
Pucci, Fabrizio; Rooman, Marianne.
  • Pucci F; Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium.
  • Rooman M; Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium.
Viruses ; 13(5)2021 05 18.
Article in English | MEDLINE | ID: covidwho-1234834
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ABSTRACT
The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions the inter-human transmissibility of the virus predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful, freely available instrument which predicts rapidly and with good accuracy the dangerousness of new viral strains. It can be integrated and play a fundamental role in the genomic surveillance programs of the SARS-CoV-2 virus that, despite all the efforts, remain time-consuming and expensive.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computational Biology / Genetic Fitness / SARS-CoV-2 Type of study: Prognostic study Topics: Variants Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: V13050935

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computational Biology / Genetic Fitness / SARS-CoV-2 Type of study: Prognostic study Topics: Variants Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: V13050935