Identification, design and synthesis of tubulin-derived peptides as novel hyaluronan mimetic ligands for the receptor for hyaluronan-mediated motility (RHAMM/HMMR)
Bibliographic record
Abstract
Fragments of the extracellular matrix component hyaluronan (HA) promote tissue inflammation, fibrosis and tumor progression. HA fragments act through HA receptors including CD44, LYVE1, TLR2, 4 and the receptor for hyaluronan mediated motility (RHAMM/HMMR). RHAMM is a multifunctional protein with both intracellular and extracellular roles in cell motility and proliferation. Extracellular RHAMM binds directly to HA fragments while intracellular RHAMM binds directly to ERK1 and tubulin. Both HA and regions of tubulin (s-tubulin) are anionic and bind to basic amino acid-rich regions in partner proteins, such as in HA and tubulin binding regions of RHAMM. We used this as a rationale for developing bioinformatics and SPR (surface plasmon resonance) based screening to identify high affinity anionic RHAMM peptide ligands. A library of 12-mer peptides was prepared based on the carboxyl terminal tail sequence of s-tubulin isoforms and assayed for their ability to bind to the HA/tubulin binding region of recombinant RHAMM using SPR. This approach resulted in the isolation of three 12-mer peptides with nanomolar affinity for RHAMM. These peptides bound selectively to RHAMM but not to CD44 or TLR2,4 and blocked RHAMM:HA interactions. Furthermore, fluorescein-peptide uptake by PC3MLN4 prostate cancer cells was blocked by RHAMM mAb but not by CD44 mAb. These peptides also reduced the ability of prostate cancer cells to degrade collagen type I. The selectivity of these novel HA peptide mimics for RHAMM suggest their potential for development as HA mimetic imaging and therapeutic agents for HA-promoted disease.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".