How SARS-CoV-2 causes a wide range of clinical manifestations and disease severity remains poorly understood. SARS-CoV-2 encodes 2 proteases (3CLPro and PLPro), vital for viral production, but also promiscuous with respect to host protein targets. Pharmacological inhibition of 3CLPro markedly reduced hospitalization and death in Phase 2/3 clinical studies. Here, we develop a bioinformatic algorithm, leveraging experimental data from SARS-CoV, to predict host cleavage targets of 3CLPro. We capture targets of 3CLPro described previously for SARS-CoV-2, as well as thousands of putative targets. We validate numerous targets cleaved during infection, including the giant sarcomeric protein obscurin and the innate immune protein OAS1. A long form of OAS1, p46, has been associated in numerous GWAS studies with lesser COVID disease severity. We show that 3CLPro cleaves p46 OAS1 immediately upstream of a known prenylation domain, relocalizing OAS1 from subcellular membranes to the cytosol, rendering it akin to the nonprotective, cytosolic p42 isoform. Similar OAS1 relocalization occurs upon infection by SARS-CoV-2. Our data provide a high-throughput resource to identify putative host cleavage targets of 3CLPro and reveal a mechanism by which SARS-CoV-2 antagonizes host innate immunity in individuals with the protective p46 isoform of OAS1.
Nora Yucel, Silvia Marchiano, Evan Tchelepi, Germana Paterlini, Ivan A. Kuznetsov, Kristina Li, Quentin McAfee, Nehaar Nimmagadda, Andy Ren, Sam Shi, Alyssa Grogan, Aikaterini Kontrogianni-Konstantopoulos, Charles Murry, Zoltan Arany
Usage data is cumulative from February 2026 through July 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 1,783 | 0 |
| 501 | 0 | |
| Figure | 1,031 | 0 |
| Table | 111 | 0 |
| Supplemental data | 1,145 | 0 |
| Citation downloads | 309 | 0 |
| Totals | 4,880 | 0 |
| Total Views | 4,880 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.