pep-05373 v1 CC-BY-SA-4.0
Beta-defensin 123 anticancer peptide
A small protein fragment studied for its ability to slow or stop cancer cell growth; experimental, not yet 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
ipTM0.000
pTM0.420
avg pLDDT50.9
ranking score0.491
STRUCTURE · PEP-05373 × ANTICANCER
ranking0.491
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
15101520253035404547
GTQRCWNLYGKCRHRC SKKERVYVYCLNNKMC CVKPKYQPKEKWWPF
in the news
details
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.49131670594215393 | boltz-2 |
▸3-letter notation
Gly-Thr-Gln-Arg-Cys-Trp-Asn-Leu-Tyr-Gly-Lys-Cys-Arg-His-Arg-Cys-Ser-Lys-Lys-Glu-Arg-Val-Tyr-Val-Tyr-Cys-Leu-Asn-Asn-Lys-Met-Cys-Cys-Val-Lys-Pro-Lys-Tyr-Gln-Pro-Lys-Glu-Lys-Trp-Trp-Pro-Phe
▸recipeboltz-2 2.2.1
| parameter | value |
|---|---|
| model | boltz-2 2.2.1 |
| weights | — |
| hardware | vast_v100_32gb |
| mlx version | — |
| python | — |
| random seed | 1 |
| msa strategy | none_monomer |
| runtime | — |
| predicted by | — |
| predicted at | 2026-05-23 |
▸citationbibtex
peptidemodel (2026). Beta-defensin 123 anticancer peptide (pep-05373, v1). PeptideModel. https://peptidemodel.com/card/pep-05373
@peptide{pep05373,
sequence = {GTQRCWNLYGKCRHRCSKKERVYVYCLNNKMCCVKPKYQPKEKWWPF},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {computed}
} related peptides
references
discussion
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