Polyphenolic Compounds Isolated from Marine Algae Attenuate the Replication of SARS-CoV-2 in the Host Cell through a Multi-Target Approach of 3CLpro and PLpro
Bibliographic record
Abstract
A global health concern has emerged as a response to the recent SARS-CoV-2 pandemic. The identification and inhibition of drug targets of SARS-CoV-2 is a decisive obligation of scientists. In addition to the cell entry mechanism, SARS-CoV-2 expresses a complicated replication mechanism that provides excellent drug targets. Papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro) play a vital role in polyprotein processing, producing functional non-structural proteins essential for viral replication and survival in the host cell. Moreover, PLpro is employed by SARS-CoV-2 for reversing host immune responses. Therefore, if some particular compound has the potential to interfere with the proteolytic activities of 3CLpro and PLpro of SARS-CoV-2, it may be effective as a treatment or prophylaxis for COVID-19, reducing viral load, and reinstating innate immune responses. Thus, the present study aims to inhibit SARS-CoV-2 through 3CLpro and PLpro using marine natural products isolated from marine algae that contain numerous beneficial biological activities. Molecular docking analysis was utilized in the present study for the initial screening of selected natural products depending on their 3CLpro and PLpro structures. Based on this approach, Ishophloroglucin A (IPA), Dieckol, Eckmaxol, and Diphlorethohydroxycarmalol (DPHC) were isolated and used to perform in vitro evaluations. IPA presented remarkable inhibitory activity against interesting drug targets. Moreover, Dieckol, Eckmaxol, and DPHC also expressed significant potential as inhibitors. Finally, the results of the present study confirm the potential of IPA, Dieckol, Eckmaxol, and DPHC as inhibitors of SARS-CoV-2. To the best of our knowledge, this is the first study that assesses the use of marine natural products as a multifactorial approach against 3CLpro and PLpro of SARS-CoV-2.
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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) | 0.000 | 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) | 0.001 | 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".