Impact of tetramerization on the ligand recognition of N1 influenza neuraminidase via MMGBSA approach
Bibliographic record
Abstract
Influenza virus neuraminidase (NA) is a homotetrameric surface protein that, in contrast to other non-influenza NAs, requires a quaternary assembly to exhibit enzymatic activity, suggesting that the oligomeric state significantly impacts the active site of influenza NA. Nevertheless, most structure-based drug design studies have been reported by employing the monomeric state in the closed or open-loop due to the computational cost of employing the tetrameric NA. In this work, we present MD simulations coupled to the MMGBSA approach of avian N1 type NA in its monomeric and tetrameric closed and open-loop state both with and without the inhibitor oseltamivir and its natural substrate, sialic acid. Structural and energetic analyses revealed that the tetrameric state impacts flexibility as well as the map of interactions participating in stabilizing the protein-ligand complexes with respect to the monomeric state. It was observed that the tetrameric state exerts dissimilar effects in binding affinity, characteristic of positive and negative cooperativity for oseltamivir and sialic acid, respectively. Based on our results, to perform a confident structure-based drug design, as well as to evaluate the impact of key mutations through MD simulations, it is important to consider the tetrameric state closed-loop state.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".