Adsorption Mechanism of Myelin Basic Protein on Model Substrates and Its Bridging Interaction between the Two Surfaces
Bibliographic record
Abstract
Myelin basic protein (MBP) is an intrinsically disordered (unstructured) protein known to play an important role in the stability of myelin's multilamellar membrane structure in the central nervous system. The adsorption of MBP and its capacity to interact with and bridge solid substrates has been studied using a surface forces apparatus (SFA) and a quartz crystal microbalance with dissipation (QCM-D). Adsorption experiments show that MBP molecules adsorb to the surfaces in a swollen state before undergoing a conformational change into a more compact structure with a thickness of ∼3 nm. Moreover, this compact structure is able to interact with nearby mica surfaces to form adhesive bridges. The measured adhesion force (energy) between two bridged surfaces is 1.0 ± 0.1 mN/m, (Ead = 0.21 ± 0.02 mJ/m(2)), which is slightly smaller than our previously reported adhesion force of 1.7 mN/m (Ead = 0.36 mJ/m(2)) for MBP adsorbed on two supported lipid bilayers (Lee et al., Proc. Natl. Acad. Sci. U.S.A. 2014, 111, E768-E775). The saturated surface concentration of compact MBP on a single SiO2 surface reaches a stable value of 310 ± 10 ng/cm(2) regardless of the bulk MBP concentration. A kinetic three-step adsorption model was developed that accurately fits the adsorption data. The developed model is a general model, not limited to intrinsically disordered proteins, that can be extended to the adsorption of various chemical compounds that undergo chemical reactions and/or conformational changes upon adsorbing to surfaces. Taken together with our previously published data (Lee et al., Proc. Natl. Acad. Sci. U.S.A. 2014, 111, E768-E775), the present results confirm that conformational changes of MBP upon adsorption are a key for strong adhesion, and that such conformational changes are strongly dependent on the nature of the surfaces.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".