bioRxivEvolutionary BiologyPreprint
Systematic screen of PKR reveals genetic variants that broadly evade divergent viral pseudosubstrate inhibitors
Michael James ChambersiD, Tristan R GrieveiD, Sophia B ScobelliD, Meru J. SadhuiD
Abstract
Evolutionary arms races can arise at the contact surfaces between host and viral proteins, producing dynamic spaces in which genetic variants are continually pursued even while protein function must be maintained. A striking case is protein kinase R (PKR), an innate immune protein that detects viral replication and halts protein synthesis by phosphorylating the initiation factor eIF2. PKR is targeted by many viral antagonists, including pseudosubstrate inhibitors encoded by poxviruses and ranaviruses that mimic eIF2. We previously found that the eIF2-binding surface of human PKR is highly malleable against the vaccinia virus pseudosubstrate inhibitor K3. Here, we screen our library of SNP-accessible PKR variants against five viral inhibitors of increasing divergence: K3 orthologs from vaccinia, variola, tanapox, and myxoma viruses, and the independently derived vIF2 from Rana catesbeiana virus Z. Resistance-conferring variants are readily accessible against every inhibitor tested and are often shared across phylogenetically diverse K3 orthologs and the ranavirus inhibitor, suggesting that PKR escape variants can exploit features common to pseudosubstrate inhibitors. Many variants beneficial against multiple inhibitors correspond to sites under positive selection across vertebrates. Inhibitor-specific effects are largely explained by differences in contact residues between PKR and each inhibitor. Notably, no PKR variant became newly susceptible to myxoma K3, which does not naturally inhibit human PKR. The eIF2-binding surface of PKR is thus broadly navigable against genetically diverse viral pseudosubstrate inhibitors, potentiating its evolutionary capacity to combat viral inhibition without incurring new vulnerabilities.
- organism: human
- assay: protein sequence
- research focus: variant effect prediction
- research focus: protein structure prediction
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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