Impact of the Temperature on the Interactions Between Common Variants of the SARS-CoV-2 Receptor Binding Domain and the Human ACE2
Bibliographic record
Abstract
Abstract Several key mutations in the Spike protein receptor binding domain (RBD) have been identified to influence its affinity for the human Angiotensin-Converting Enzyme 2 (ACE2). Here, we perform a comparative study of the ACE2 binding to the wild type (Wuhan) RBD and some of its variants: Alpha B.1.1.7, Beta B.1.351, Delta B.1.617.2, Kappa B.1.617.1, B.1.1.7+L452R and Omicron B.1.1.529. Using a coiled-coil mediated tethering approach of ACE2 in a novel surface plasmon resonance (SPR)-based assay, we measured interactions at different temperatures. Binding experiments at 10°C enhanced the kinetic dissimilarities between the RBD variants and allowed a proper fit to a Langmuir 1:1 model with high accuracy and reproducibility. The Omicron RBD variant has the highest affinity for ACE2, followed by the Alpha (N501Y) and Beta (K417N, E484K, N501Y) variants. However, variants with the L452R mutation, i.e. the B.1.1.7+L452R (L452R, N501Y; which we called AlphaP), Delta (L452R, T478K) and Kappa (L452R, E484Q) showed faster kinetics resulting in significatively lower affinities compared to the other variants. Our study emphasizes the importance of SPR-based assay parameters in the acquisition of quality data and offers a powerful tool to deepen our understanding of the role of the various RBD mutations in ACE2 interaction binding parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".