Exploring the Role of Glycans in the Interaction of SARS-CoV-2 RBD and Human Receptor ACE2
Bibliographic record
Abstract
COVID-19 is a highly infectious respiratory disease caused by the novel coronavirus SARS-CoV-2. It has become a global pandemic and its frequent mutations may pose new challenges for vaccine design. During viral infection, the Spike RBD of SARS-CoV-2 binds the human host cell receptor ACE2, enabling the virus to enter the host cell. Both the Spike and ACE2 are densely glycosylated, and it is unclear how distinctive glycan types may modulate the interaction of RBD and ACE2. Detailed understanding of these determinants is key for the development of novel therapeutic strategies. To this end, we perform extensive all-atom simulations of the (i) RBD-ACE2 complex without glycans, (ii) RBD-ACE2 with oligomannose MAN9 glycans in ACE2, and (iii) RBD-ACE2 with complex FA2 glycans in ACE2. These simulations identify the key residues at the RBD-ACE2 interface that form contacts with higher probabilities, thus providing a quantitative evaluation that complements recent structural studies. Notably, we find that this RBD-ACE2 contact signature is not altered by the presence of different glycoforms, suggesting that RBD-ACE2 interaction is robust. Applying our simulated results, we illustrate how the recently prevalent N501Y mutation may alter specific interactions with host ACE2 that facilitate the virus-host binding. Furthermore, our simulations reveal how the glycan on Asn90 of ACE2 can play a distinct role in the binding and unbinding of RBD. Finally, an energetics analysis shows that MAN9 glycans on ACE2 decrease RBD-ACE2 affinity, while FA2 glycans lead to enhanced binding of the complex. Together, our results provide a more comprehensive picture of the detailed interplay between virus and human receptor, which is much needed for the discovery of effective treatments that aim at modulating the physical-chemical properties of this virus.
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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".