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As the major target of many vaccines and neutralizing antibodies againstSARS-CoV-2, the spike (S) protein is observed to mutate over time. In thispaper, we present statistical approaches to tackle some challenges associatedwith the analysis of S-protein data. We build a Bayesian hierarchical model tostudy the temporal and spatial evolution of S-protein sequences, after groupingthe sequences into representative clusters. We then apply sampling methods toinvestigate possible changes to the S-protein's 3-D structure as a result ofcommonly observed mutations. While the increasing spread of D614G variants hasbeen noted in other research, our results also show that the co-occurringmutations of D614G together with S477N or A222V may spread even more rapidly,as quantified by our model estimates.

Statistical challenges in the analysis of sequence and structure data for the COVID-19 spike protein