Binding strength and hydrogen bond numbers between Covid-19 RBD and HVR of antibody
Bibliographic record
Abstract
Abstract The global battle against the Covid-19 pandemic relies strongly on the human defence of antibody, which is assumed to bind the antigen’s Receptor Binding Domain with its Hypervariable Region. Due to the similarity to other viruses such as SARS, however, our understanding of the antibody-virus interaction has been largely limited to the genomic sequencing, which poses serious challenges to the containment, vaccine exploration and rapid serum testing. Based on the physical/chemical nature of the interaction, infrared spectroscopy was employed to reveal the binding disparity, when unusual temperature dependence was discovered from the 1550cm -1 absorption band, attributed to the hydrogen bonds by carboxyl/amino groups, binding the SARS-CoV-2 spike protein and closely resembled SARS-CoV-2 or SARS-CoV-1 antibodies. The infrared absorption intensity, associated with the number of hydrogen bonds, was found to increase sharply between 27°C and 31°C, with the relative absorbance matches at 37°C the hydrogen bonding numbers of the two antibody types (19 vs 12). Meanwhile the ratio of bonds at 27°C, calculated by thermodynamic exponentials rather than by the layman’s guess, produces at least 5% inaccuracy. As a result, the specificity of the SARS-CoV-2 antibody will be more conclusive beyond 31°C, instead of at the usual room temperature of 20°C - 25°C, when the vaccine research and antibody diagnosis would likely be undermined. Beyond genomic sequencing, the temperature dependence, as well as the bond number match at 37°C between relative absorbance and the hydrogen bonding numbers of the two antibody types, are not only of clinical significance in particular, but also of a sample for the physical/chemical understanding of the vaccine-antibody interactions in general.
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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) | 0.004 | 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".