Aegerolysin anticancer peptide
An experimental peptide studied for its ability to fight cancer cells; not 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.
A chemistry service or a researcher ordered the sequence, it was manufactured, and mass spectrometry confirmed the right molecule was produced.
A binding or activity measurement confirmed that it actually does what the computer predicted — or didn't.
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.
What if this peptide kills cancer cells by latching onto fats in the cell membrane rather than the protein locks that most cancer drugs try to fit?
If true, the entire way researchers study and develop this peptide would need to change, using membrane-fat assays instead of the standard protein-docking approach. It would also place pep-05318 in a different competitive landscape from conventional protein-targeted cancer peptides.
Could connecting the two loose ends of this peptide into a closed loop stop the body from breaking it down so fast?
Linear peptides like this one typically fall apart in the bloodstream within two to thirty minutes, which makes them nearly impossible to use as drugs. If cyclizing pep-05318 extends that window while keeping its cancer-cell-killing ability intact, it could become a realistic candidate for testing in animals and eventually in people.
What if instead of jamming a biological signal, this peptide simply punches holes in the outer wall of cancer cells until they collapse?
If pep-05318 works by rupturing the membrane directly, it could explain why it hits a broad range of cancer types, and it would be much harder for cancer cells to evolve resistance against than drugs that target a single protein. For patients, that could mean a treatment that stays effective longer and covers more cancer variants.
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.5667165517807007 | 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{pep05318,
sequence = {MDSNKDERAYAQWVIIILHNVGSSPFKIANLGLSWGKLYADGNKDKEVY},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {computed}
}