Journal of Data Science ›› 2020, Vol. 18 ›› Issue (3): 511-525.doi: 10.6339/JDS.202007_18(3).0017

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Assessing the Impacts of Mutations to the Structure of COVID-19 Spike Protein via Sequential Monte Carlo

Samuel W. K. Wong   

  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
  • Online:2020-07-21 Published:2020-07-22

Abstract: Proteins play a key role in facilitating the infectiousness of the 2019 novel coronavirus. A specific spike protein enables this virus to bind to human cells, and a thorough understanding of its 3-dimensional structure is therefore critical for developing effective therapeutic interventions. However, its structure may continue to evolve over time as a result of mutations. In this paper, we use a data science perspective to study the potential structural impacts due to ongoing mutations in its amino acid sequence. To do so, we identify a key segment of the protein and apply a sequential Monte Carlo sampling method to detect possible changes to the space of low- energy conformations for different amino acid sequences. Such computational approaches can further our understanding of this protein structure and complement laboratory efforts.

Key words: conformational sampling, protein structure prediction, SARS-CoV-2