Statistical Challenges in the Analysis of Sequence and Structure Data for the COVID-19 Spike Protein

He, Shiyu; Wong, Samuel W.K.

doi:10.6339/21-JDS1006

Journal of Data Science

Statistical Challenges in the Analysis of Sequence and Structure Data for the COVID-19 Spike Protein

Volume 19, Issue 2 (2021): Special issue: Continued Data Science Contributions to COVID-19 Pandemic, pp. 314–333

Shiyu He Samuel W.K. Wong

https://doi.org/10.6339/21-JDS1006

Pub. online: 22 February 2021 Type: COVID-19 Special Issue

Received
31 December 2020

Accepted
18 January 2021

Published
22 February 2021

Abstract

As the major target of many vaccines and neutralizing antibodies against SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this paper, we present statistical approaches to tackle some challenges associated with the analysis of S-protein data. We build a Bayesian hierarchical model to study the temporal and spatial evolution of S-protein sequences, after grouping the sequences into representative clusters. We then apply sampling methods to investigate possible changes to the S-protein’s 3-D structure as a result of commonly observed mutations. While the increasing spread of D614G variants has been noted in other research, our results also show that the co-occurring mutations of D614G together with S477N or A222V may spread even more rapidly, as quantified by our model estimates.

Supplementary material

Supplementary Material

The processed data, R code, and instructions for reproducing the results in this paper are provided in a supplementary .zip file.

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Keywords

Bayesian hierarchical models compositional data analysis conformational sampling mutant clusters SARS-CoV-2

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