Identification of novel allosteric sites of SARS-CoV-2 papain-like protease (PLpro) for the development of COVID-19 antivirals
Bibliographic record
Abstract
Coronaviruses such as SARS-CoV-2 encode a conserved papain-like protease (PLpro) that is crucial for viral replication and immune evasion, making it a prime target for antiviral drug development. In this study, three surface pockets on SARS-CoV-2 PLpro that may function as sites for allosteric inhibition were computationally identified. To evaluate the effects of these pockets on proteolytic activity, 52 residues were separately mutated to alanine. In Pocket 1, located between the Ubl and thumb domains, the introduction of alanine at T10, D12, T54, Y72, or Y83 reduced PLpro activity to <12% of that of WT. In Pocket 2, situated at the interface of the thumb, fingers, and palm domains, Q237A, S239A, H275A, and S278A inactivated PLpro. Finally, introducing alanine at five residues in Pocket 3, between the fingers and palm domains, inactivated PLpro: S212, Y213, Y251, K254, and Y305. Pocket 1 has a higher druggability score than Pockets 2 and 3. MD simulations showed that interactions within and between domains play critical roles in PLpro activity and thermal stability. The essential residues in Pockets 1 and 2 participate in a combination of intra- and inter-domain interactions. By contrast, the essential residues in Pocket three predominantly participate in inter-domain interactions. The most promising targets for therapeutic development are Pockets one and 3, which have the highest druggability score and the largest number of essential residues, respectively. Non-competitive inhibitors targeting these pockets may be antiviral agents against COVID-19 and related coronaviruses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".