Predicting mammalian species at risk of being infected by SARS-CoV-2 from an ACE2 perspective
Bibliographic record
Abstract
SARS-CoV-2 can transmit efficiently in humans, but it is less clear which other mammals are at risk of being infected. SARS-CoV-2 encodes a Spike (S) protein that binds to human ACE2 receptor to mediate cell entry. A species with a human-like ACE2 receptor could therefore be at risk of being infected by SARS-CoV-2. We compared between 132 mammalian ACE2 genes and between 17 coronavirus S proteins. We showed that while global similarities reflected by whole ACE2 gene alignments are poor predictors of high-risk mammals, local similarities at key S protein-binding sites highlight several high-risk mammals that share good ACE2 homology with human. Bats are likely reservoirs of SARS-CoV-2, but there are other high-risk mammals that share better ACE2 homologies with human. Both SARS-CoV-2 and SARS-CoV are closely related to bat coronavirus. Yet, among host-specific coronaviruses infecting high-risk mammals, key ACE2-binding sites on S proteins share highest similarities between SARS-CoV-2 and Pangolin-CoV and between SARS-CoV and Civet-CoV. These results suggest that direct coronavirus transmission from bat to human is unlikely, and that rapid adaptation of a bat SARS-like coronavirus in different high-risk intermediate hosts could have allowed it to acquire distinct high binding potential between S protein and human-like ACE2 receptors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".