Biosynthetic Short Neuropeptides: A Rational Theory Based on Experimental Results for the Missing Pain‐Relief Opioid Endomorphin Precursor Gene
Bibliographic record
Abstract
Endomorphins are neuropeptides that bind strongly to μ-opioid receptors and are considered to play important roles in pain modulation and other biological functions. Two endomorphins have been identified, to date, endomorphine-1 and -2; both are tetrapeptides and differ by only a single amino acid in the third position. Both peptides were isolated from bovine brains; however, their precursor genes have not been identified. In this study, a nucleotide sequence corresponding to the endomorphin-1 peptide in an expressed sequence tag database has been found and a preproendomorphin-like precursor peptide from human brain complementary DNA (cDNA) has been cloned. The cDNA consists of nucleotide sequences of two already annotated predicted genes, and the putative peptide differs by one amino acid from the isolated endomorphin peptides. It is proposed herein that there is the possibility of unknown short proteins or peptide precursors being missed by automated gene prediction programs based on similarities of known protein sequences. A novel concept of how to produce endomorphins from a similar peptide is described. The oxidatively modified base might provide a clue for understanding discrepancies between nucleotide sequences on the genome and those on cDNAs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".