Therapeutic potential of metal ions for COVID-19: insights from the papain-like protease of SARS-CoV-2
Bibliographic record
Abstract
Coronaviruses have been responsible for multiple challenging global pandemics, including coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Papain-like protease (PLpro), one of two cysteine proteases responsible for the maturation and infectivity of SARS-CoV-2, processes and liberates functional proteins from the viral polyproteins and cleaves ubiquitin and ISG15 modifications to inhibit innate immune sensing. Consequently, PLpro is an attractive target for developing COVID-19 therapies. PLpro contains a zinc-finger domain important for substrate binding and structural stability. However, the impact of metal ions on the activity and biophysical properties of SARS-CoV-2 PLpro has not been comprehensively studied. Here, we assessed the impacts of metal ions on the catalytic activity of PLpro. Zinc had the largest inhibitory effect on PLpro, followed by manganese. Calcium, magnesium, and iron had smaller or no effects on PLpro activity. EDTA at a concentration of 0.5 mM was essential for PLpro activity, likely by chelating trace metals that inhibit PLpro. IC50 values for ZnCl2, ZnSO4, and MnCl2 of 0.42 ± 0.02 mM, 0.35 ± 0.01 mM, and 2.6 ± 0.3 mM were obtained in the presence of 0.5 mM EDTA; in the absence of EDTA, the estimated IC50 of ZnCl2 was 14 µM. Tryptophan intrinsic fluorescence analysis confirmed the binding of zinc and manganese to PLpro, and differential scanning calorimetry revealed that zinc but not manganese reduced ΔHcal of PLpro. The results of this study provide a reference for further work targeting PLpro to prevent and treat COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".