Identification of residues in the receptor-binding domain (RBD) of the spike protein of human coronavirus NL63 that are critical for the RBD–ACE2 receptor interaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human coronavirus NL63 (NL63), a member of the group I coronaviruses, may cause acute respiratory diseases in young children and immunocompromised adults. Like severe acute respiratory syndrome coronavirus (SARS-CoV), NL63 also employs the human angiotensin-converting enzyme 2 (hACE2) receptor for cellular entry. To identify residues in the spike protein of NL63 that are important for hACE2 binding, this study first generated a series of S1-truncated variants, examined their associations with the hACE2 receptor and subsequently mapped a minimal receptor-binding domain (RBD) that consisted of 141 residues (aa 476-616) towards the C terminus of the S1 domain. The data also demonstrated that the NL63 RBD bound to hACE2 more efficiently than its full-length counterpart and had a binding efficiency comparable to the S1 or RBD of SARS-CoV. A further series of RBD variants was generated using site-directed mutagenesis and random mutant library screening assays, and identified 15 residues (C497, Y498, V499, C500, K501, R518, R530, V531, G534, G537, D538, S540, E582, W585 and T591) that appeared to be critical for the RBD-hACE2 association. These critical residues clustered in three separate regions (designated RI, RII and RIII) inside the RBD, which may represent three receptor-binding sites. These results may help to delineate the molecular interactions between the S protein of NL63 and the hACE2 receptor, and may also enhance our understanding of the pathogenesis of NL63 and SARS-CoV.
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.
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 | 0.002 | 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 it