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
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.
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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) | 0.001 | 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".