Mass spectrometry-based proteomics in basic and translational research of SARS-CoV-2 coronavirus and its emerging mutants
Bibliographic record
Abstract
Molecular diagnostics of the coronavirus disease of 2019 (COVID-19) now mainly relies on the measurements of viral RNA by RT-PCR, or detection of anti-viral antibodies by immunoassays. In this review, we discussed the perspectives of mass spectrometry-based proteomics as an analytical technique to identify and quantify proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to enable basic research and clinical studies on COVID-19. While RT-PCR and RNA sequencing are indisputably powerful techniques for the detection of SARS-CoV-2 and identification of the emerging mutations, proteomics may provide confirmatory diagnostic information and complimentary biological knowledge on protein abundance, post-translational modifications, protein-protein interactions, and the functional impact of the emerging mutations. Pending advances in sensitivity and throughput of mass spectrometry and liquid chromatography, shotgun and targeted proteomic assays may find their niche for the differential quantification of viral proteins in clinical and environmental samples. Targeted proteomic assays in combination with immunoaffinity enrichments also provide orthogonal tools to evaluate cross-reactivity of serology tests and facilitate development of tests with the nearly perfect diagnostic specificity, this enabling reliable testing of broader populations for the acquired immunity. The coronavirus pandemic of 2019-2021 is another reminder that the future global pandemics may be inevitable, but their impact could be mitigated with the novel tools and assays, such as mass spectrometry-based proteomics, to enable continuous monitoring of emerging viruses, and to facilitate rapid response to novel infectious diseases.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".