Development of FERM domain protein-protein interaction inhibitors for MSN and CD44 as a potential therapeutic strategy for Alzheimer’s disease
Bibliographic record
Abstract
Abstract Recent genome-wide association studies have revealed genetic risk factors for Alzheimer’s disease (AD) that are exclusively expressed in microglia within the brain. A proteomics approach identified moesin (MSN), a FERM (four-point-one ezrin radixin moesin) domain protein, and the receptor CD44 as hub proteins found within a co-expression module strongly linked to AD clinical and pathological traits as well as microglia. The FERM domain of MSN interacts with the phospholipid PIP 2 and the cytoplasmic tails of receptors such as CD44. This study explored the feasibility of developing protein-protein interaction inhibitors that target the MSN–CD44 interaction. Structural and mutational analyses revealed that the FERM domain of MSN binds to CD44 by incorporating a beta strand within the F3 lobe. Phage-display studies identified an allosteric site located close to the PIP 2 binding site in the FERM domain that affects CD44 binding within the F3 lobe. These findings support a model in which PIP 2 binding to the FERM domain stimulates receptor tail binding through an allosteric mechanism that causes the F3 lobe to adopt an open conformation permissive for binding. High-throughput screening of a chemical library identified two compounds that disrupt the MSN–CD44 interaction, and one compound series was further optimized for biochemical activity, specificity, and solubility. The results suggest that the FERM domain holds potential as a drug development target. The small molecule preliminary leads generated from the study could serve as a foundation for additional medicinal chemistry effort with the goal of controlling microglial activity in AD by modifying the MSN–CD44 interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".