Monoclonal antibody designed for SARS-nCoV-2 spike protein of receptor binding domain on antigenic targeted epitopes for inhibition to prevent viral entry
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome (SARS) is developing disease caused by novel coronavirus (COVID19). This situation has urgently prompted many pharma and R&D research companies and public research health sectors to concentrate their efforts on research for effective therapeutics. SARS-nCoV-2 as a spike (S) protein was a targeted for the development of monoclonal antibody and therapeutics for the prevention and treatment. We developed monoclonal antibody by deep mutational scanning to characterize the effects of mutations in an antibody variable fragment on its based on expression levels, specificity, stability, and affinity for antigenic specific epitope to the Spike-S-RBD. Further to make this antibody multipoint core mutations to improve contacts between specific Fv light and heavy chains to the targeted antigen of RBD. This antibody combined enhancing mutations yielding with higher binding affinity and substantially improved stability in between RBD and antibody. Overall, this antibody may well prevent into the cell interaction with RBD-hACE2 to viral entry and prevention. SARS-nCoV-Spike-S monoclonal antibodies potently inhibited SARS-nCoV-2-Spike-S mediated entry into cells, indicating that cross-neutralizing antibodies targeting conserved S epitopes can be elicited based upon vaccination.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".