  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.
