Identification of polypeptides with selective affinity to intact mouse cerebellar granule neurons from a random peptide-presenting phage library: J.Neurosci.Methods
Bibliographic record
Abstract
Targeting of postmitotic neurons selectively for gene delivery poses a challenge. One way to achieve such a selective targeting is to link the gene delivery vector with small ligand-binding polypeptides which have selective affinity to intact neurons. In order to identify such novel neuron selective polypeptides, we screened a phage-display library displaying random 12-mer polypeptides and subtractively bio-panned for clones having selectivity towards cultured mouse cerebellar granule neurons. The selected phage clones were amplified and sequenced. Affinities of these clones to neurons were determined by the visible presence or absence of fluorescence of phage particles as detected by immunocytochemistry using an antibody to M-13 phage. This affinity was further qualified by how much phage was bound, and where in or on the cell it tended to accumulate. The selectivity of binding to neurons was determined by the negative binding of these clones to several cultured non-neuronal cells, including, primary glial cells, NT2 cells, human embryonic kidney 293 cells, neuroblastoma cells, and mouse 3T3 cells. Among the 46 clones that we have sequenced and characterized, four clones appeared to have excellent selectivity in binding to neurons. Homology comparison of these polypeptides revealed that three of them contained a consensus D(E)-W(F)-I(N)-D-W motif. This motif was also present in the Bdm1 gene product which was predominantly expressed in postnatal brains. Further characterizations of these polypeptides are required to reveal the utilities of these peptides to function as an effective linker to facilitate gene transfer selectively to neurons
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".