Decoding protein networks during virus entry by quantitative proteomics
Bibliographic record
Abstract
Virus entry into host cells relies on interactions between viral and host structures including lipids, carbohydrates and proteins. Particularly, protein-protein interactions between viral surface proteins and host proteins as well as secondary host protein-protein interactions play a pivotal role in coordinating virus binding and uptake. These interactions are dynamic and frequently involve multiprotein complexes. In the past decade mass spectrometry based proteomics methods have reached sensitivities and high throughput compatibilities of genomics methods and now allow the reliable quantitation of proteins in complex samples from limited material. As proteomics provides essential information on the biologically active entity namely the protein, including its posttranslational modifications and its interactions with other proteins, it is an indispensable method in the virologist's toolbox. Here we review protein interactions during virus entry and compare classical biochemical methods to study entry with novel technically advanced quantitative proteomics techniques. We highlight the value of quantitative proteomics in mapping functional virus entry networks, discuss the benefits and limitations and illustrate how the methodology will help resolve unsettled questions in virus entry research in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".