In Silico Modeling of Myelin Oligodendrocyte Glycoprotein Disulfide Bond Reduction by Phosphine-Borane Complexes
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative diseases can cause vision loss by damaging retinal ganglion cells in the optic nerve. Novel phosphine-borane compounds (PBs) can protect these cells from oxidative stress via the reduction of disulfide bonds. However, the specific targets of these compounds are unknown. Proteomic evidence suggests that myelin oligodendrocyte glycoprotein (MOG) is a potential target. MOG is of significant interest due to its role in anti-MOG optic neuritis syndrome. METHODS: We used in silico modeling to explore the structural consequences of cleaving the extracellular domain MOG disulfide bond, both in isolation and in complex with anti-MOG antibodies. The potential binding of PBs to this bond was examined using molecular docking. RESULTS: Cleaving the disulfide bond of MOG altered the structure of MOG dimers and reduced their energetic favorability by 46.13 kcal/mol. The energy profiles of anti-MOG antibody complexes were less favorable when the disulfide bond of MOG was reduced in the monomeric state by 55.21 kcal/mol, but the reverse was true in the dimeric state. PBs exhibited reducing capabilities with the MOG extracellular disulfide bond, with this best-scoring compound binding with an energy of -28.54 kcal/mol to the MOG monomer and -24.97 kcal/mol to the MOG dimer. CONCLUSIONS: These findings suggest that PBs can affect the structure of MOG dimers and the formation of antibody complexes by reducing the MOG disulfide bond. Structural changes in MOG could have implications for neurodegenerative diseases and anti-MOG syndrome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".