pep-05119 v1 CC-BY-SA-4.0
PBAN metabolic peptide
Targets the same receptor as diabetes and weight-loss drugs like Ozempic; studied as a possible metabolic treatment, not an approved medicine.
status
Someone proposed this peptide.
A researcher, an agent, or an algorithm wrote down the sequence and picked a target to hit.
A computer predicted how the peptide binds to its target.
An AI model like OpenFold3 or AlphaFold built a 3D structure and scored how well it fits the binding site.
Someone else ran the same prediction and got the same result.
A second contributor repeated the computation on their own hardware and the scores matched.
The peptide was actually made in a lab.
A chemistry service or a researcher ordered the sequence, it was manufactured, and mass spectrometry confirmed the right molecule was produced.
The peptide was tested on its target in a lab.
A binding or activity measurement confirmed that it actually does what the computer predicted — or didn't.
prediction metrics
ipTM0.825
pTM0.808
avg pLDDT70.8
ranking score0.731
STRUCTURE · PEP-05119 × GLP-1R
ranking0.731
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
15101520253033
LADDMPATMAD QEVYRPEPEQI DSRNKYFSPRL
in the news
details
▸full evidence table2 metrics
| metric | value | tool |
|---|---|---|
| ipTM | 0.8252310156822205 | boltz-2 |
| ranking score | 0.731449544429779 | boltz-2 |
▸3-letter notation
Leu-Ala-Asp-Asp-Met-Pro-Ala-Thr-Met-Ala-Asp-Gln-Glu-Val-Tyr-Arg-Pro-Glu-Pro-Glu-Gln-Ile-Asp-Ser-Arg-Asn-Lys-Tyr-Phe-Ser-Pro-Arg-Leu
▸recipeboltz-2 1.0
| parameter | value |
|---|---|
| model | boltz-2 1.0 |
| weights | — |
| hardware | nvidia_nim_api |
| mlx version | — |
| python | — |
| random seed | — |
| msa strategy | colabfold_nvidia |
| diffusion samples | 1 |
| runtime | — |
| predicted by | mlx@peptide |
| predicted at | 2026-04-26 |
▸citationbibtex
peptidemodel (2026). PBAN metabolic peptide (pep-05119, v1). PeptideModel. https://peptidemodel.com/card/pep-05119
@peptide{pep05119,
sequence = {LADDMPATMADQEVYRPEPEQIDSRNKYFSPRL},
target = {glp-1r},
author = {peptidemodel},
year = {2026},
status = {computed}
} references
discussion
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