Role of α and β Transmembrane Domains in Integrin Clustering
Bibliographic record
Abstract
Integrins are transmembrane proteins playing a crucial role in the mechanical signal transduction from the outside to the inside of a cell, and vice versa. Nevertheless, this signal transduction could not be implemented by a single protein. Rather, in order for integrins to be able to participate in signal transduction, they need to be activated and produce clusters first. As integrins consist of α- and β-subunits that are separate in the active state, studying both subunits separately is of a great importance, for, in the active state, the distance between α- and β-subunits is long enough that they do not influence one another significantly. Thus, this study aims to investigate the tendency of transmembrane domains of integrins to form homodimers. We used both Steered and MARTINI Coarse-grained molecular dynamics method to perform our simulations, mainly because of a better resolution and computational feasibility that each of these methods could provide to us. Using the Steered molecular dynamics method for α- and β-subunits, we found that the localized lipid packing prevented them from clustering. Nonetheless, the lipid packing phenomenon was found to be an artifact after investigating this process using a coarse grained (CG) model. Exploiting the coarse-grained molecular dynamics simulations, we found that α- and β-subunits tend to form a stable homo-dimer.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".