Cheminformatics Bioprospection of Broad Spectrum Plant Secondary Metabolites Targeting the Spike Proteins of Omicron Variant and Wild-Type SARS-CoV-2
Bibliographic record
Abstract
The spike protein (SP) of SARS-CoV-2 (SC-2) is susceptible to high mutation and has contributed to the multiple waves of COVID-19 being experienced. Hence, targeting the SP remains a logical approach in the development of potent therapeutics against SARS-CoV-2. Here, a computational technique was adopted to identify broad-spectrum plant secondary metabolites with indigenous relevance in the management of respiratory infections against the SPs of the SC-2 wild- type (SC-2WT) and omicron variants. Following 100 ns molecular dynamic (MD) simulation and binding free energy calculation of the top five compounds identified through molecular docking, maysin (SC-2WT (-34.85 kcal/mol), omicron (-38.88 kcal/mol)) and geraniin (SC-2WT (-36.90 kcal/mol) omicron (-31.28 kcal/mol)) had better broad-spectrum activities for the investigated SPs than zafirlukast (SC-2WT (-33.73 kcal/mol) omicron (-22.38 kcal/mol)). Furthermore, 6-hydroxycyanidin-3-rutinoside (-42.97 kcal/mol) and kaempferol-7-glucoside (-37.11 kcal/mol) had the best affinity for the SPs of omicron and SC-2WT, respectively. Interestingly, except for Kaempferol-7-glucoside against omicron SP, all the top-ranked compounds were thermodynamically stable with the SP of both variants, and this observation was linked to the number, nature, and bond length in the resulting complexes in each case. Also, except for geraniin, all the top-ranked compounds had lower toxicity profiles compared to zafirlukast and this could be attributed to their phenolic moieties. Nevertheless, the in vitro and in vivo confirmation of the activities observed in this study is recommended, especially for maysin and geraniin with the best broad-spectrum activity, towards development of COVID-19 drug candidates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".