Muscle-growth booster peptide: follistatin fragment (1-27)
A naturally occurring fragment of follistatin that blocks myostatin and related signals, freeing muscles to grow; 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.
Does this 28-amino-acid piece actually bind the proteins it is supposed to block?
If the fragment cannot block its targets, researchers could redirect effort toward redesigning it as a scaffold instead of using the native piece.
▸full evidence table2 metrics
| metric | value | tool |
|---|---|---|
| ipTM | 0.2262854129076004 | boltz-2 |
| ranking score | 0.6240484118461609 | 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 | colabfold_local |
| runtime | — |
| predicted by | — |
| predicted at | 2026-05-22 |
▸citationbibtex
@peptide{pep10791,
sequence = {GNDLCYEPCECFEDLECNPESTQYEDEC},
target = {activin-a},
author = {peptidemodel},
year = {2026},
status = {bioassayed}
}