Deciphering the structural and functional impact of Q657L mutation in <i>NLRC4</i> using computational methods
Bibliographic record
Abstract
The NLR family caspase recruitment domain-containing protein 4 (NLRC4) inflammasome regulates the inflammatory response. Upregulation of NLRC4 inflammasome has been associated with bacterial infections, cancers, and autoinflammatory disorders (AIDs). The Q657L mutation in the NLRC4 causes AID. However, the structural change upon Q657L mutation at the atomic level has not been clearly understood. In this study, we employed in silico predictions along with molecular dynamics (MD) simulations of the homology modelled human wild-type and mutant NLRC4 structures in the resting and activated state to investigate the impact of Q657L mutation on the structural and dynamic changes of NLRC4 protein. The Q657L mutation was predicted to be deleterious by various in silico prediction tools. The MD simulation results demonstrated that the mutation increased the stability of the compactly folded structure and decreased flexibility in the resting state. In the activated state, the stably folded mutant structure had increased solvent accessible surface area, intermolecular hydrogen bonds and binding pocket volume. In addition, the principal component analysis showed that the mutant structures had reduced dynamics in both states. These findings provide insights into structural and dynamic changes of NLRC4 protein due to Q657L mutation at the atomic level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".