Discovery of FERM domain protein–protein interaction inhibitors for MSN and CD44 as a potential therapeutic approach for Alzheimer’s disease
Bibliographic record
Abstract
Proteomic studies have identified moesin (MSN), a protein containing a four-point-one, ezrin, radixin, moesin (FERM) domain, and the receptor CD44 as hub proteins found within a coexpression module strongly linked to Alzheimer's disease (AD) traits and microglia. These proteins are more abundant in Alzheimer's patient brains, and their levels are positively correlated with cognitive decline, amyloid plaque deposition, and neurofibrillary tangle burden. The MSN FERM domain interacts with the phospholipid phosphatidylinositol 4,5-bisphosphate (PIP 2 ) and the cytoplasmic tail of CD44. Inhibiting the MSN–CD44 interaction may help limit AD-associated neuronal damage. Here, we investigated the feasibility of developing inhibitors that target this protein–protein interaction. We have employed structural, mutational, and phage-display studies to examine how CD44 binds to the FERM domain of MSN. Interestingly, we have identified an allosteric site located close to the PIP 2 binding pocket that influences CD44 binding. These findings suggest a mechanism in which PIP 2 binding to the FERM domain stimulates CD44 binding through an allosteric effect, leading to the formation of a neighboring pocket capable of accommodating a receptor tail. Furthermore, high-throughput screening of a chemical library identified two compounds that disrupt the MSN–CD44 interaction. One compound series was further optimized for biochemical activity, specificity, and solubility. Our results suggest that the FERM domain holds potential as a drug development target. Small molecule preliminary leads generated from this study could serve as a foundation for additional medicinal chemistry efforts 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 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) | 0.001 | 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 | 0.001 |
| 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".