文章基本信息

标题：Assessing the Impacts of Mutations to the Structure of COVID-19 Spike Protein via Sequential Monte Carlo
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作者：Samuel W. K. Wong
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2020
卷号：18
期号：3
页码：511-525
DOI：10.6339/JDS.202007_18(3).0017
出版社：Tingmao Publish Company
摘要：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 lowenergy conformations for different amino acid sequences. Such computational approaches can further our understanding of this protein structure and complement laboratory efforts.
关键词：conformational sampling; protein structure prediction; SARS-CoV-2