Biochemical and biophysical characterization of the main protease, 3-chymotrypsin-like protease (3CLpro) from the novel coronavirus SARS-CoV 2
Bibliographic record
Abstract
Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is responsible for the novel coronavirus disease 2019 (COVID-19). An appealing antiviral drug target is the coronavirus 3C-like protease (3CLpro) that is responsible for the processing of the viral polyproteins and liberation of functional proteins essential for the maturation and infectivity of the virus. In this study, multiple thermal analytical techniques have been implemented to acquire the thermodynamic parameters of 3CLpro at different buffer conditions. 3CLpro exhibited relatively high thermodynamic stabilities over a wide pH range; however, the protease was found to be less stable in the presence of salts. Divalent metal cations reduced the thermodynamic stability of 3CLpro more than monovalent cations; however, altering the ionic strength of the buffer solution did not alter the stability of 3CLpro. Furthermore, the most stable thermal kinetic stability of 3CLpro was recorded at pH 7.5, with the highest enthalpy of activation calculated from the slope of Eyring plot. The biochemical and biophysical properties of 3CLpro explored here may improve the solubility and stability of 3CLpro for optimum conditions for the setup of an enzymatic assay for the screening of inhibitors to be used as lead candidates in the discovery of drugs and design of antiviral therapeutics against COVID-19.
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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 | 0.000 | 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 | 0.000 |
| 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 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".