pep-05113 v1 CC-BY-SA-4.0
SFTPC metabolic peptide
An experimental peptide that acts on a gut hormone receptor tied to blood sugar and weight control; not an approved drug.
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
ipTM0.538
pTM0.787
avg pLDDT75.1
ranking score0.709
STRUCTURE · PEP-05113 × GLP-1R
ranking0.709
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
15101520253034
GIPCFPSSLKRLLIIVV VIVLVVVVIVGALLMGL
in the news
details
▸full evidence table2 metrics
| metric | value | tool |
|---|---|---|
| ipTM | 0.5384812355041504 | boltz-2 |
| ranking score | 0.708646297454834 | boltz-2 |
▸3-letter notation
Gly-Ile-Pro-Cys-Phe-Pro-Ser-Ser-Leu-Lys-Arg-Leu-Leu-Ile-Ile-Val-Val-Val-Ile-Val-Leu-Val-Val-Val-Val-Ile-Val-Gly-Ala-Leu-Leu-Met-Gly-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). SFTPC metabolic peptide (pep-05113, v1). PeptideModel. https://peptidemodel.com/card/pep-05113
@peptide{pep05113,
sequence = {GIPCFPSSLKRLLIIVVVIVLVVVVIVGALLMGL},
target = {glp-1r},
author = {peptidemodel},
year = {2026},
status = {computed}
} references
discussion
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