The effect of cysteine oxidation on conformational changes of SARS-CoV-2 spike protein using atomistic simulations
Bibliographic record
Abstract
The SARS-CoV-2 Spike (S) protein plays a central role in viral entry into host cells, making it a key target for therapeutic interventions. Oxidative stress, often triggered during viral infections, can cause oxidation of cysteine in this protein. Here we investigate the impact of cysteine oxidation, specifically the formation of cysteic acid, on the conformational dynamics of the SARS-CoV-2 S protein using atomistic simulations. In particular, we examine how cysteine oxidation influences the transitions of the S protein’s receptor-binding domain (RBD) between “down” (inaccessible) and “up” (accessible) states, which are critical for host cell receptor engagement. Using solvent-accessible surface area (SASA) analysis, we identify key cysteine residues susceptible to oxidation. The results of targeted molecular dynamics (TMD) and umbrella sampling (US) simulations reveal that oxidation reduces the energy barrier for RBD transitions by approximately 30 kJ mol−1, facilitating conformational changes and potentially enhancing viral infectivity. Furthermore, we analyze the interactions between oxidized cysteine residues and glycans, as well as alterations in hydrogen bonds and salt bridges. Our results show that oxidation disrupts normal RBD dynamics, influencing the energy landscape of conformational transitions. Our work provides novel insights into the role of cysteine oxidation in modulating the structural dynamics of the SARS-CoV-2 S protein, highlighting potential targets for antiviral strategies aimed at reducing oxidative stress or modifying post-translational changes. These findings contribute to a deeper understanding of viral infectivity and pathogenesis under oxidative conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".