Binding Strength and Hydrogen Bond Numbers between COVID-19 RBD and HVR of Antibody
Bibliographic record
Abstract
The global battle against the COVID-19 pandemic relies strongly on the human defense of antibody, which is assumed to bind the antigen’s receptor binding domain (RBD) with its hypervariable region (HVR). 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 containment and rapid serum testing. Based on the physical/chemical nature of the interaction, infrared spectroscopy was employed to reveal the binding disparity, the real cause of the antibody-virus specificity at the molecular level, which is inconceivable to be investigated otherwise. Temperature dependence was discovered in the absorption value from the 1550 cm−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 matching the hydrogen bonding numbers of the two antibody types (19 vs. 12) at 37 °C. Meanwhile, the ratio of bonds at 27 °C, calculated by thermodynamic exponentials, produces at least 5% inaccuracy. 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, is not only of clinical significance in particular but also as a sample for the physical/chemical understanding of vaccine–antibody interactions in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".