Waprin-Phi3 anticancer peptide
An experimental peptide studied for its ability to attack 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.
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 this protein stop cancer by targeting a specific enzyme, rather than just attacking cells blindly?
Many cancer enzymes called proteases act like scissors that let tumors cut through tissue and spread. If Waprin-Phi3 works by jamming one of these scissors, it would be a more precise tool than a general cell-killer, which could mean fewer side effects and a clearer path to combining it with other drugs. This is unproven, but the structure of the molecule fits the job in a way that makes it worth testing.
Could a single molecule tackle both antibiotic-resistant infections and cancer at the same time?
Cancer patients undergoing treatment often have weakened immune systems and are especially vulnerable to dangerous drug-resistant bacteria like MRSA. If Waprin-Phi3 turns out to be active against both, it could potentially address two serious threats at once for the same patient. That is still a hypothesis, but it would open a much wider development path than cancer alone.
Could this molecule last long enough in the body to actually reach and affect a tumor?
Most small protein drugs fall apart in blood within minutes before they reach a tumor. The tight knot-like structure of Waprin-Phi3 might shield it from the enzymes that would normally chew it up. If that holds, it could work when injected into the bloodstream, which is a much more practical route than local injection and a bar most similar molecules fail to clear.
Is it the overall folded shape of the molecule, rather than its positive charge, that does the actual work?
Knowing what part of a molecule drives its effect matters enormously for making better versions. If the folded structure is what kills cancer cells, then a simpler version of the molecule without those internal bonds might be inactive, and the design rules change completely. It also raises a practical question: the tumor environment is chemically reducing, which could unfold the molecule and either switch it off or, potentially, trigger a release effect.
Could scientists attach a targeting signal to this molecule so it only attacks a chosen type of cancer?
Antibody-drug conjugates, the current gold standard for targeted cancer delivery, are expensive and complex to manufacture. A small, rugged protein scaffold that is already toxic to cancer cells, and could be fitted with a homing tag in one of its flexible loops, would be a much simpler alternative. This is an engineering hypothesis, not a proven capability, but the structural precedent from related proteins exists.
Does this molecule naturally gravitate toward cancer cells because of a marker that healthy cells keep hidden?
A known weakness of cancer-killing molecules is that they can attack healthy cells too. Most cancer cells expose a fat molecule called phosphatidylserine on their outer surface, which normal cells keep tucked inside. If Waprin-Phi3 is preferentially drawn to that exposed signal, it could explain why it might be selective, and would mean its activity would not depend on any single cancer mutation, potentially making it relevant across many tumor types.
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.5345109105110168 | 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{pep05220,
sequence = {KTTKQLRLPKVKPGECPKVKIPPDYPCNQYCVWDFDCEGNKKCCPVGCAKECFPPGPL},
target = {anticancer},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
}