Analysis of the SARS-CoV-2-host protein interaction network reveals new biology and drug candidates: focus on the spike surface glycoprotein and RNA polymerase
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic originated from the emergence of anovel coronavirus, SARS-CoV-2, which has been intensively studied since its discovery in order to generate the knowledge necessary to accelerate the development of vaccines and antivirals. Of note, many researchers believe there is great potential in systematically identifying host interactors of viral factors already targeted by existing drugs.Areas Covered: Herein, the authors discuss in detail the only available large-scale systematic study of the SARS-CoV-2-host protein–protein interaction network. More specifically, the authors review the literature on two key SARS-CoV-2 drug targets, the Spike surface glycoprotein, and the RNA polymerase. The authors also provide the reader with their expert opinion and future perspectives.Expert opinion: Interactions made by viral proteins with host factors reveal key functions that are likely usurped by the virus and, as aconsequence, points to known drugs that can be repurposed to fight viral infection and collateral damages that can exacerbate various disease conditions in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".