Mutation Signatures and In Silico Docking of Novel SARS-CoV-2 Variants of Concern
Bibliographic record
Abstract
One year since the first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in China, several variants of concern (VOC) have appeared around the world, with some variants seeming to pose a greater thread to public health due to enhanced transmissibility or infectivity. This study provides a framework for molecular characterization of novel VOC and investigates the effect of mutations on the binding affinity of the receptor-binding domain (RBD) to human angiotensin-converting enzyme 2 (hACE2) using in silico approach. Notable nonsynonymous mutations in RBD of VOC include the E484K and K417N/T that can be seen in South African and Brazilian variants, and N501Y and D614G that can be seen in all VOC. Phylogenetic analyses demonstrated that although the UK-VOC and the BR-VOC fell in the clade GR, they have different mutation signatures, implying an independent evolutionary pathway. The same is true about SA-VOC and COH-VOC felling in clade GH, but different mutation signatures. Combining molecular interaction modeling and the free energy of binding (FEB) calculations for VOC, it can be assumed that the mutation N501Y has the highest binding affinity in RBD for all VOC, followed by E484K (only for BR-VOC), which favors the formation of a stable complex. However, mutations at the residue K417N/T are shown to reduce the binding affinity. Once vaccination has started, there will be selective pressure that would be in favor of the emergence of novel variants capable of escaping the immune system. Therefore, genomic surveillance should be enhanced to find and monitor new emerging SARS-CoV-2 variants before they become a public health concern.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".