SARS-CoV-2 ORF8 sequence conservation and mutational analysis — insight into the influence of dataset size on identifying top mutations
Bibliographic record
Abstract
Abstract Given how quickly the SARS-CoV-2 virus mutates, the COVID-19 pandemic has been a major source of concern. The ORF8 accessory protein is one such protein, which is reported to have undergone many mutations. This makes ORF8 an intriguing protein to investigate how these mutations might play a role in overall ORF8 activity. In this study, we have performed conservation and mutational analysis on SARS-CoV- 2 ORF8 protein sequences to identify the conserved and mutated residues. We have also split the ORF8 sequence data into SARS-CoV-2 variant datasets to further identify top mutations across each of them. The mutated and conserved residues were visualised on the available structure of ORF8 to highlight the conserved and mutated sites, which might hold some biological significance. Finally, our study also investigated the significance of sequence dataset size in capturing top mutations following multiple sequence alignments. Author Summary The COVID-19 pandemic was caused by the SARS-CoV-2 virus, which is known to change over time, i.e., it gets mutated, resulting in the generation of different variants. The ORF8 accessory protein of the SARS-CoV- 2 genome is known to undergo these changes more frequently. In our study, we used SARS-CoV-2 ORF8 protein sequences from various variants to identify mutations among them. Furthermore, we have discovered sites that remain unchanged over time, a phenomenon known as conservation. We think that these unchanged and changed sites could be important for biology and studying them will help in understanding the underlying mechanism of how ORF8 interacts with partner proteins based on existing experimental data. Lastly, we have looked at how much sequence data is sufficient for identifying the top mutated sites. Graphical abstract
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".