pep-05378 v1 CC-BY-SA-4.0
Cationic antimicrobial protein anticancer peptide
A lab-made peptide being studied for its potential to fight cancer cells; experimental, 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
ipTM0.000
pTM0.500
avg pLDDT71.2
ranking score0.670
STRUCTURE · PEP-05378 × ANTICANCER
ranking0.670
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
15101520253035404547
DEEKRENEDEENQEDD EQSEMRRGLRSKIWLW VLLMIWQESNKFKKM
in the news
details
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.670001745223999 | boltz-2 |
▸3-letter notation
Asp-Glu-Glu-Lys-Arg-Glu-Asn-Glu-Asp-Glu-Glu-Asn-Gln-Glu-Asp-Asp-Glu-Gln-Ser-Glu-Met-Arg-Arg-Gly-Leu-Arg-Ser-Lys-Ile-Trp-Leu-Trp-Val-Leu-Leu-Met-Ile-Trp-Gln-Glu-Ser-Asn-Lys-Phe-Lys-Lys-Met
▸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). Cationic antimicrobial protein anticancer peptide (pep-05378, v1). PeptideModel. https://peptidemodel.com/card/pep-05378
@peptide{pep05378,
sequence = {DEEKRENEDEENQEDDEQSEMRRGLRSKIWLWVLLMIWQESNKFKKM},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {computed}
} related peptides
references
discussion
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