Beta-defensin 2 anticancer peptide
A lab-made peptide being studied for its potential to attack cancer cells; experimental, 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.
Does this peptide need its full complex structure to attack cancer, or could a simpler version work just as well?
If the full 3D structure turns out not to be needed, this peptide could be made faster, cheaper, and more consistently in a lab, which could lower the cost and speed up early research for anyone developing it as a cancer therapy.
Could just the front half of this peptide attack cancer cells, and might it work even better inside the acidic environment of a tumor?
If a shorter fragment works as well as the full molecule, it would be far easier and cheaper to produce. The bonus: tumors are more acidic than normal tissue, and one amino acid in this fragment could become more active in that environment, potentially giving it a built-in way to seek out tumors while leaving healthy tissue alone.
Could one peptide both punch holes in cancer cells and send a distress signal that brings the immune system running?
If this dual action holds up, the peptide could be injected directly into a tumor, killing cells while also turning that site into a kind of on-the-spot vaccine. This approach sidesteps the hard problem of getting a peptide drug to survive in the bloodstream, and it could complement existing immunotherapy strategies.
Instead of just punching holes in cells at random, does this peptide actually dock with a specific target that cancer cells have in unusually high numbers?
If a defined receptor turns out to be the key, researchers could move from trial-and-error to rational drug design: tweaking the peptide to fit that receptor better, identifying which cancer types are most likely to respond, and building a clearer case for clinical testing.
Could a single peptide tackle a cancer site while also clearing up the bacterial infections that frequently develop there?
Patients with ulcerating tumors or post-surgical wounds often face polymicrobial infections that standard antibiotics cannot clear. If this peptide proves active against both cancer cells and bacteria like Staph and Pseudomonas, it could become a practical topical treatment for an underserved and difficult clinical problem, where simpler formulations are acceptable and full drug stability is less critical.
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.5400311350822449 | 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{pep05291,
sequence = {AVGSLKSIGYEAELDHCHTNGGYCVRAICPPSARRPGSCFPEKNPCCKYMK},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {computed}
}