Neuropeptide FF (FLFQPQRF-NH2) and its Fragments Bind to α2δ Subunit of Voltage-Gated Calcium Channels
Bibliographic record
Abstract
PURPOSE: Gabapentin, a drug for neuropathic pain, exerts its therapeutic effect via inhibition of the a2d subunit of N-type Ca2+ channels. Thus, finding peptides that specifically displace gabapentin from its binding site may lead to the development of new drugs. METHODS: Displacement of bound [3H]-gabapentin in membrane preparations of rat cerebral cortex and of human Cav2.2/β3/α2δ1 expressed in CHO cell line. RESULTS: Neuropeptide FLFQPQRF-NH2 specifically displaced bound [3H]-gabapentin in membrane preparations from rats and CHO cells. Truncation of the C-terminus of FLFQPQRF-NH2 by three amino acid residues to produce FLFQP-NH2 improved the displacement of gabapentin. FLFQP-NH2 displaced bound [3H]-gabapentin with IC50 and Ki values of 2.7 µM and 1.7 µM, respectively. Deletion of two amino acid residues (FQ) in the middle of the FLFQP-NH2 sequence yielded FLP-NH2 that displaced bound [3H]-gabapentin with a lower affinity. IC50 and Ki values were 11.9 µM and 7.8 µM, respectively. Neutral binding cooperativity existed when of FLFQP-NH2, FLP- NH2 and gabapentin when incubated together. FLFQPQRF-NH2 but not FLFQP-NH2 displaced bound [3H]- gabapentin to membrane preparations of human Cav2.2/b3/a2d1 expressed in CHO cells. CONCLUSION: FLFQPQRF-NH2, FLFQP-NH2 and FLP-NH2 displace bound gabapentin in membrane preparations of rat cerebral cortex. Binding cooperativity was detected when GBP/FLFQP-NH2/FLP-NH2 were incubated together. These novel binding sites may provide new approaches to modulate L-type Ca2+ channels.
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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) | 0.000 | 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) | 0.002 | 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".