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