SARS-CoV2 (COVID-19) Structural/Evolution Dynamicome: Insights into functional evolution and human genomics

Abstract

The SARS-CoV-2 pandemic, starting in 2019, has challenged the speed at which labs perform science, ranging from discoveries of the viral composition to handling health outcomes in humans. The small ~30kb single-stranded RNA genome of Coronaviruses makes them adept at cross species spread and drift, increasing their probability to cause pandemics. However, this small genome also allows for a robust understanding of all proteins coded by the virus. We employed protein modeling, molecular dynamic simulations, evolutionary mapping, and 3D printing to gain a full proteome and dynamicome understanding of SARS-CoV-2. The Viral Integrated Structural Evolution Dynamic Database (VIStEDD) has been established (prokoplab.com/vistedd), opening future discoveries and educational usage. In this paper, we highlight VIStEDD usage for nsp6, Nucleocapsid (N), and Spike (S) surface glycoprotein. For both nsp6 and N we reveal highly conserved surface amino acids that likely drive protein-protein interactions. In characterizing viral S protein, we have developed a quantitative dynamics cross correlation matrix insight into interaction with the ACE2/SLC6A19 dimer complex. From this quantitative matrix, we elucidated 47 potential functional missense variants from population genomic databases within ACE2/SLC6A19/TMPRSS2, warranting genomic enrichment analyses in SARS-CoV-2 patients. Moreover, these variants have ultralow frequency, but can exist as hemizygous in males for ACE2, which falls on the X-chromosome. Two noncoding variants (rs4646118 and rs143185769) found in ~9% of African descent individuals for ACE2 may regulate expression and be related to increased susceptibility of African Americans to SARS-CoV-2. This powerful database of SARS-CoV-2 can aid in research progress in the ongoing pandemic.

Genetic Analysis of the Coronavirus SARS-CoV-2 Host Protease TMPRSS2 in Different Populations

“…Our findings do not support the existence of common coding pathogenic variants for TMPRSS2 among different populations. Accordingly, recent studies identified only a few pathogenic, ultra-rare variants in this gene (Gupta et al, 2020 ; Paniri et al, 2020 ; Sajuthi et al, 2020 ). Therefore, given their rarity, we do not believe that coding variants, except for some rare cases, can determine the diverse susceptibility to viral infection and diverse clinical manifestations.…”

“…All of them exhibit very low AFs ( Figure 1A ). Accordingly, a recent study identified a few functional ultra-rare variants in TMPRSS2 , all of them with AFs < 0.001 (Gupta et al, 2020 ). These findings agree with the recommended benign frequency cut-off of 0.0001 for the TMPRSS2 gene, as from the Varsome database.…”

Missense mutations in SARS-CoV2 genomes from Indian patients

“…This study of mutations in the SARS-CoV2 genomes of 128 COVID-19 affected Indian patients has been carried out. Various mutations over the proteins of SARS-CoV2 genomes are presented in the articles [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. A comprehensive list of patients with other associated details are presented in the Table 1 .…”

A 3D Structural Interactome to Explore the Impact of Evolutionary Divergence, Population Variation, and Small-molecule Drugs on SARS-CoV-2-Human Protein-Protein Interactions

“…After constructing the 3D interactome between SARS-CoV-2 and human, we first looked for evidence of interface-specific variation by mapping both gnomAD 58 reported human population variants (Supplemental Table 3) and sequence divergences between SARS-CoV and SARS-CoV-2 (Supplemental Table 4) onto the predicted interfaces. In general, conserved residues have been shown to cluster at protein-protein interfaces 63 , and a recent analysis of SARS-CoV-2 structure and evolution likewise concluded that highly conserved surface residues were likely to drive protein-protein interactions 64 . Consistent with these prior findings at an interactome-wide level, we observed significant depletion for both viral and human variation along the predicted interfaces comparable to that observed on solved human-human interfaces (Fig 2.a).…”