Mapping allosteric pathway in NIa‐Pro using computational approach
Bibliographic record
Abstract
Background Computer simulation studies complement in vitro experiments and provide avenue to understand allosteric regulation in the absence of other molecular viewing techniques. Molecular dynamics captures internal motion within the protein and enables tracing the communication path between a catalytic site and a distal allosteric site. In this article, we have identified the communication pathway between the viral protein genome linked (VPg) binding region and catalytic active site in nuclear inclusion protein‐a protease (NIa‐Pro). Methods Molecular dynamics followed by in silico analyses have been used to map the allosteric pathway. Results This study delineates the residue interaction network involved in allosteric regulation of NIa‐Pro activity by VPg. Simulation studies indicate that point mutations in the VPg interaction interface of NIa‐Pro lead to disruption in these networks and change the orientation of catalytic residues. His142Ala and His167Ala mutations do not show a substantial change in the overall protease structure, but rather in the residue interaction network and catalytic site geometry. Conclusion Our mutagenic study delineates the allosteric pathway and facilitates the understanding of the modulation of NIa‐Pro activity on a molecular level in the absence of the structure of its complex with the known regulator VPg. Additionally, our in silico analysis explains the molecular concepts and highlights the dynamics behind the previously reported wet lab study findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".