Identification of Lysine Acetylation Sites on MERS-CoV Replicase pp1ab
Bibliographic record
Abstract
MERS is a life-threatening disease and MERS-CoV has the potential to cause the next pandemic. Protein acetylation is known to play a crucial role in host response to viral infection. Acetylation of viral proteins encoded by other RNA viruses have been reported to affect viral replication. It is therefore of interest to see whether MERS-CoV proteins are also acetylated. Viral proteins obtained from infected cells were trypsin-digested into peptides. Acetylated peptides were enriched by immunoprecipitation and subject to nano-LC-Orbitrap analysis. Bioinformatic analysis was performed to assess the conservation level of identified acetylation sites and to predict the upstream regulatory factors. A total of 12 acetylation sites were identified from 7 peptides, which all belong to the replicase polyprotein pp1ab. All identified acetylation sites were found to be highly conserved across MERS-CoV sequences in NCBI database. Upstream factors, including deacetylases of the SIRT1 and HDAC families as well as acetyltransferases of the TIP60 family, were predicted to be responsible for regulating the acetylation events identified. Western blotting confirms that acetylation events indeed occur on pp1ab protein by expressing NSP4 in HEK293 cells. Acetylation events on MERS-CoV viral protein pp1ab were identified for the first time, which indicate that MERS-CoV might use the host acetylation machinery to regulate its enzyme activity and to achieve optimal replication. Upstream factors were predicted, which might facilitate further analysis of the regulatory mechanism of MERS-CoV replication.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".