Beta-defensin 110 antimicrobial peptide
A short protein fragment that kills or disables bacteria and other microbes; used only as a lab research tool.
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.
Is the disordered region in this peptide actually what makes it effective against many different microbes?
If true, scientists could redesign just the flexible tail to make the peptide more potent or safer, without dismantling the rest of its structure. This would speed up antibiotic development by giving engineers a clear target region to modify.
Could changing a structural weak point in this peptide let it stay active inside the protective slime that bacteria use to resist treatment?
Bacterial biofilms protect many chronic infections, from wound infections to implant-associated infections, and are notoriously hard to treat. A version of this peptide engineered to stay active inside biofilms could give doctors a new option for patients whose infections keep coming back.
Does this peptide automatically become stronger at the acidic pH of an infected wound or inflamed tissue?
If the peptide activates itself at the acidic conditions found inside infected tissue, it could kill bacteria where they live while sparing healthy tissue nearby. For patients, this would mean a more targeted antibiotic with fewer side effects.
▸full evidence table1 metrics
| metric | value | tool |
|---|---|---|
| ranking score | 0.4351818561553955 | 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{pep05686,
sequence = {LLTARKRFPHYGSVDMRRECAKGNGRCKTECHISEVRIAYCIRPGSLCCLQKYR},
target = {antimicrobial},
author = {peptidemodel},
year = {2026},
status = {computed}
}