Comparative analysis of SARS-CoV-2 envelope viroporin mutations from COVID-19 deceased and surviving patients revealed implications on its ion-channel activities and correlation with patient mortality
Bibliographic record
Abstract
One major obstacle in designing a successful therapeutic regimen to combat COVID-19 pandemic is the frequent occurrence of mutations in the SARS-CoV-2 resulting in patient to patient variations. Out of the four structural proteins of SARS-CoV-2 namely, spike, envelope, nucleocapsid and membrane, envelope protein governs the virus pathogenicity and induction of acute-respiratory-distress-syndrome which is the major cause of death in COVID-19 patients. These effects are facilitated by the viroporin (ion-channel) like activities of the envelope protein. Our current work reports metagenomic analysis of envelope protein at the amino acid sequence level through mining all the available SARS-CoV-2 genomes from the GISAID and coronapp servers. We found majority of mutations in envelope protein were localized at or near PDZ binding motif. Our analysis also demonstrates that the acquired mutations might have important implications on its structure and ion-channel activity. A statistical correlation between specific mutations (e.g. F4F, R69I, P71L, L73F) with patient mortalities were also observed, based on the patient data available for 18,691 SARS-CoV-2-genomes in the GISAID database till 30 April 2021. Albeit, whether these mutations exist as the cause or the effect of co-infections and/or co-morbid disorders within COVID-19 patients is still unclear. Moreover, most of the current vaccine and therapeutic interventions are revolving around spike protein. However, emphasizing on envelope protein’s (1) conserved epitopes, (2) pathogenicity attenuating mutations, and (3) mutations present in the deceased patients, as reported in our present study, new directions to the ongoing efforts of therapeutic developments against COVID-19 can be achieved by targeting envelope viroporin. Communicated by Ramaswamy H. Sarma
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".