Identification of Human Host Substrates of the SARS-CoV-2 M<sup>pro</sup> and PL<sup>pro</sup> Using Subtiligase N-Terminomics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The recent emergence of SARS-CoV-2 in the human population has caused a global pandemic. The virus encodes two proteases, M pro and PL pro, that are thought to play key roles in the suppression of host protein synthesis and immune response evasion during infection. To identify the specific host cell substrates of these proteases, active recombinant SARS-CoV-2 M pro and PL pro were added to A549 and Jurkat human cell lysates, and subtiligase-mediated N-terminomics was used to capture and enrich protease substrate fragments. The precise location of each cleavage site was identified using mass spectrometry. Here, we report the identification of over 200 human host proteins that are potential substrates for SARS-CoV-2 M pro and PL pro and provide a global mapping of proteolysis for these two viral proteases in vitro. Modulating proteolysis of these substrates will increase our understanding of SARS-CoV-2 pathobiology and COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".