pep-10463 v1 CC-BY-SA-4.0
Nerve-signaling peptide (CHEMBL2112592)
A short experimental peptide that interacts with a nerve-cell receptor involved in pain and inflammation signaling; used only as a lab research tool.
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
ipTM0.965
pTM0.874
avg pLDDT67.8
ranking score0.736
STRUCTURE · PEP-10463 × TACR2
ranking0.736
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
15
PGVGG
in the news
details
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| Ki | 27000 nM | GPCRDB/ChEMBL |
▸structural qualityopenfold3
| metric | value | note |
|---|---|---|
| gpde | 0.922 | global PDE — lower = better |
| disorder | NaN | fraction disordered |
▸3-letter notation
Pro-Gly-Val-Gly-Gly
▸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
peptidemodel (2026). Nerve-signaling peptide (CHEMBL2112592) (pep-10463, v1). PeptideModel. https://peptidemodel.com/card/pep-10463
@peptide{pep10463,
sequence = {PGVGG},
target = {tacr2},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
} clinical trials
0
no registered clinical trials
as of 2026-05-22; we'll re-check periodically
references
0
no peer-reviewed references
this sequence isn't grounded in any published literature.
discussion
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