bioRxivMolecular BiologyPreprint
Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic
Joseph H. LubiniD, Christine ZardeckiiD, Elliott M. Dolan, Changpeng LuiD, Zhuofan SheniD, Shuchismita DuttaiD, John WestbrookiD, Brian P. HudsoniD, David S. GoodselliD, Jonathan K. WilliamsiD, Maria VoigtiD, Vidur SarmaiD, Lingjun Xie, Thejasvi Venkatachalam, Steven Arnold, Luz Helena Alfaro AlvaradoiD, Kevin C. CatalfanoiD, Aaliyah KhaniD, Erika McCarthyiD, Sophia Staggers, Brea Tinsley, Alan Trudeau, Jitendra Pratap SinghiD, Lindsey Whitmore, Helen Zheng, , Stephen Kevin BurleyiD
Abstract
Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
- organism: virus
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- Citations
- 26
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