IGF2 opioid receptor peptide
A brain-signaling molecule that acts on the body's opioid system, which helps control pain and mood; used only as a lab research tool.
A researcher, an agent, or an algorithm wrote down the sequence and picked a target to hit.
An AI model like OpenFold3 or AlphaFold built a 3D structure and scored how well it fits the binding site.
A second contributor repeated the computation on their own hardware and the scores matched.
A chemistry service or a researcher ordered the sequence, it was manufactured, and mass spectrometry confirmed the right molecule was produced.
A binding or activity measurement confirmed that it actually does what the computer predicted — or didn't.
Research directions for this peptide, selected from the current sources — hypotheses you can explore and model. None of it is proven yet; tap any one to see the full thinking.
Could a peptide that attaches to a different part of the opioid receptor produce pain relief without the usual downsides?
Most opioid drugs lose effectiveness over time and carry addiction risk because they plug into the same exact spot on the receptor every time. If this peptide latches onto a different region, it might dampen pain signals without triggering that same cycle of tolerance, though this is still an early, unproven idea.
What if the receptor this peptide is supposed to act on is simply the wrong one?
If the scientific record has pointed researchers toward the wrong receptor, every experiment and dollar spent developing this peptide as an opioid drug would be wasted. Getting the target right first is the single most important step before any further work, and fixing it early could redirect effort toward the receptor where the peptide might actually work.
Could a peptide derived from a food protein help people feel full and eat less, through the gut's own opioid system?
If this holds, it could lead to a food-grade ingredient, think a functional food or supplement, that helps reduce calorie intake without a prescription drug. It would work through a different biological route than existing approaches like probiotic bacteria that mimic appetite hormones, potentially offering an additional tool for people managing their weight.
Could a peptide reduce hunger by acting only inside the gut, sending a fullness signal to the brain without being absorbed?
Opioid drugs taken systemically cause addiction, breathing problems, and severe constipation because they act throughout the body and brain. A peptide too large to survive digestion intact might still flip gut-wall receptors that send a 'stop eating' message along the vagus nerve, potentially delivering an appetite-suppressing benefit with a much safer profile. This is speculative and needs direct testing.
If most of a long peptide is just inert filler, could you chop it down to the working piece and get a more useful molecule?
Long peptides are fragile, they break down in the gut, are expensive to make, and rarely become medicines on their own. If the biologically active part turns out to be just the aromatic-rich tail end of the sequence, a shorter version could survive digestion better and be a realistic starting point for a drug or supplement, though activity of the trimmed version still needs to be confirmed in lab experiments.
▸full evidence table2 metrics
| metric | value | tool |
|---|---|---|
| ipTM | 0.6317421793937683 | boltz-2 |
| ranking score | 0.6963351368904114 | boltz-2 |
▸structural qualityopenfold3
| metric | value | note |
|---|---|---|
| gpde | 1.063 | global PDE — lower = better |
| disorder | NaN | fraction disordered |
▸3-letter notation
▸recipeboltz-2 1.0
| parameter | value |
|---|---|
| model | boltz-2 1.0 |
| weights | — |
| hardware | nvidia_nim_api |
| mlx version | — |
| python | — |
| random seed | — |
| msa strategy | none |
| diffusion samples | 1 |
| runtime | — |
| predicted by | mlx@peptide |
| predicted at | 2026-04-24 |
▸citationbibtex
@peptide{pep05454,
sequence = {DVSASTTVLPDDVTAYPVGKFFQYDIWKQSTQRL},
target = {oprm1},
author = {peptidemodel},
year = {2026},
status = {computed}
}