Computational and Structural Biotechnology JournalBioinformaticsPeer-reviewed journal article
Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses
Christopher A. BeaudoiniD, Arian R. JamasbiD, Ali F. AlsulamiiD, Liviu CopoiuiD, Andries J. van TonderiD, Sharif M. HalaiD, Bridget P. BannermaniD, S.E. ThomasiD, Sundeep Chaitanya VedithiiD, Pedro Henrique Monteiro TorresiD, Tom L. BlundelliD
Abstract
Viruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism of the virus. Here, we use structural bioinformatics software to characterize potential mimicry of the three coronavirus spike protein receptor-binding motifs. We utilize sequence-independent alignment tools to compare structurally known or predicted three-dimensional protein models with the receptor-binding motifs and verify potential mimicry with protein docking simulations. Both human and non-human proteins were found to be similar to all three receptor-binding motifs. Similarity to human proteins may reveal which pathways the spike protein is co-opting, while analogous non-human proteins may indicate shared host interaction partners and overlapping antibody cross-reactivity. These findings can help guide experimental efforts to further understand potential interactions between human and coronavirus proteins. HighlightsO_LIPotential coronavirus spike protein mimicry revealed by structural comparison C_LIO_LIHuman and non-human protein potential interactions with virus identified C_LIO_LIPredicted structural mimicry corroborated by protein-protein docking C_LIO_LIEpitope-based alignments may help guide vaccine efforts C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/441187v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1f09454org.highwire.dtl.DTLVardef@19a5557org.highwire.dtl.DTLVardef@158d3fdorg.highwire.dtl.DTLVardef@c59511_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- Citations
- 38
- FWCI
- 2.70
- Field percentile
- top 8%
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