pep-05250 v1 CC-BY-SA-4.0
Moricin-2 cancer-fighting peptide
A peptide studied in the lab for its ability to fight cancer cells; 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.
SYNTHESIZED — literature evidence
Literature-extracted sequence peptide — synthesized for bioassay as documented in linked reference(s)
Fork this card to add platform evidence →
BIOASSAYED — literature evidence
Activity measured in linked reference(s) — IC50/MIC/cytotoxicity data
Fork this card to add platform evidence →
prediction metrics
ipTM0.000
pTM0.258
avg pLDDT49.6
ranking score0.449
STRUCTURE · PEP-05250 × ANTICANCER
ranking0.449
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
1510152025303540455054
AMSLVSCSTAAPAKIPIK AIKTVGKAVGKGLRAINI ASTANDVFNFLKPKKRKH
in the news
details
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.4485081732273102 | boltz-2 |
▸3-letter notation
Ala-Met-Ser-Leu-Val-Ser-Cys-Ser-Thr-Ala-Ala-Pro-Ala-Lys-Ile-Pro-Ile-Lys-Ala-Ile-Lys-Thr-Val-Gly-Lys-Ala-Val-Gly-Lys-Gly-Leu-Arg-Ala-Ile-Asn-Ile-Ala-Ser-Thr-Ala-Asn-Asp-Val-Phe-Asn-Phe-Leu-Lys-Pro-Lys-Lys-Arg-Lys-His
▸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). Moricin-2 cancer-fighting peptide (pep-05250, v1). PeptideModel. https://peptidemodel.com/card/pep-05250
@peptide{pep05250,
sequence = {AMSLVSCSTAAPAKIPIKAIKTVGKAVGKGLRAINIASTANDVFNFLKPKKRKH},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
} related peptides
references
discussion
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