Which mammalian species are at risk of being infected by SARS-CoV-2: an ACE2 perspective
Bibliographic record
Abstract
Abstract SARS-CoV-2 can transmit efficiently in humans, but it is less clear what other mammalian are at high risk of being infected. SARS-CoV-2 contain a Spike (S) protein that uses mammalian ACE2 receptors to mediate cell entry, a species with a human-like ACE2 receptor is therefore at risk of being infected by SARS-CoV-2. We compared between 131 mammalian ACE2 genes and 15 coronavirus S proteins. We showed that global similarity reflected by the phylogenetic relationship from ACE2 gene alignment is a poor predictor of high-risk mammals, whereas local ACE2 similarities at key binding sites highlight several high-risk mammals. Both SARS-CoV and SARS-CoV-2 likely have a bat origin; however, direct human transmission is unlikely due to their differences in ACE2 receptors, and various mammals share similar or better homologies in ACE2 receptor with humans. Furthermore, by comparing key binding sites at S protein of SARS-like coronaviruses in high-risk mammals, we found high similarities in S protein binding domains between SARS-CoV-2 and Pangolin-CoV but not Civets-CoV, and high similarities between SARS-CoV and Civets-CoV but not Pangolin-CoV. Hence, evolutionary adaptation of the bat virus in different intermediate hosts could allow 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".