Dermaseptin-S11 anticancer peptide
A peptide studied for its ability to fight cancer cells; experimental, not yet an approved drug.
A researcher, an agent, or an algorithm wrote down the sequence and picked a target to hit.
An AI model like OpenFold3 or AlphaFold built a 3D structure and scored how well it fits the binding site.
A second contributor repeated the computation on their own hardware and the scores matched.
Literature-extracted sequence peptide — synthesized for bioassay as documented in linked reference(s)
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Activity measured in linked reference(s) — IC50/MIC/cytotoxicity data
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Research directions for this peptide, selected from the current sources — hypotheses you can explore and model. None of it is proven yet; tap any one to see the full thinking.
Could trimming a useless chunk off a natural frog peptide and replacing it with a tumor-homing tag make it far better at killing cancer cells without harming healthy ones?
If this engineering step works, it could turn a weak, unfocused peptide into one that homes to tumors and attacks them at a much lower dose, which would matter for people with solid cancers and for researchers trying to shrink both the cost and side effects of peptide-based therapies.
Could the charged tail of this peptide act like a safety lock, keeping it harmless in healthy tissue and only releasing its cell-killing ability in the acidic conditions found inside tumors?
If true, the peptide would behave like a built-in smart drug that activates only where it is needed, potentially reducing damage to healthy tissue, which is one of the biggest unsolved problems with existing cancer-killing peptides.
Could this peptide tell cancer cells apart from normal ones by latching onto a fat molecule that cancer cells display on their surface but healthy red blood cells keep tucked away inside?
If the selectivity mechanism holds up, it would explain how to design safer peptide therapies that kill tumors without destroying red blood cells, addressing a key safety problem that has held back this entire class of compounds.
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.5897356271743774 | boltz-2 |
▸3-letter notation
▸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
@peptide{pep05261,
sequence = {EEEKRENEDEEEQEDDEQSEEKRALWKTLLKGAGKVFGHVAKQFLGSQGQPES},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
}