All-Atom Molecular Dynamics Simulations of Dimeric Lung Surfactant Protein B in Lipid Multilayers
Bibliographic record
Abstract
Although lung surfactant protein B (SP-B) is an essential protein that plays a crucial role in breathing, the details of its structure and mechanism are not well understood. SP-B forms covalent homodimers, and in this work we use all-atom molecular dynamics simulations to study dimeric SP-B's structure and its behavior in promoting lipid structural transitions. Four initial system configurations were constructed based on current knowledge of SP-B's structure and mechanism, and the protein maintained a helicity consistent with experiment in all systems. Several SP-B-induced lipid reorganization behaviors were observed, and regions of the protein particularly important for these activities included SP-B's "central loop" and "hinge" regions. SP-B dimers with one subunit initially positioned in each of two adjacent bilayers appeared to promote close contact between two bilayers. When both subunits were initially positioned in the same bilayer, SP-B induced the formation of a defect in the bilayer, with water penetrating into the centre of the bilayer. Similarly, dimeric SP-B showed a propensity to interact with preformed interpores in the bilayer. SP-B dimers also promoted bilayer thinning and creasing. This work fleshes out the atomistic details of the dimeric SP-B structures and SP-B/lipid interactions that underlie SP-B's essential functions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".