Quantifying the adhesive strength between the SARS-CoV-2 S-proteins and human receptor and its effect in therapeutics
Bibliographic record
Abstract
The binding affinity and adhesive strength between the spike (S) glycoproteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the human angiotensin-converting enzyme 2 (ACE2) receptor is computed using molecular dynamics (MD) simulations. The calculations indicate that the binding affinity is [Formula: see text] [Formula: see text] with a maximum adhesive force of [Formula: see text] pN. Our analysis suggests that only 27 (13 in S-protein, 14 in ACE2) residues are active during the initial fusion process between the S-protein and ACE2 receptor. With these insights, we investigated the effect of possible therapeutics in the size and wrapping time of virus particles by reducing the binding energy. Our analysis indicates that this energy has to be reduced significantly, around 50% or more, to block SARS-CoV-2 particles with radius in the order of [Formula: see text] nm. Our study provides concise target residues and target binding energy reduction between S-proteins and receptors for the development of new therapeutics treatments for COVID-19 guided by computational design.
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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) | 0.001 | 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".