pep-04990 v1 CC-BY-SA-4.0
Blood-pressure-lowering peptide (AAPLAP)
A small peptide that blocks ACE, the enzyme that raises blood pressure; experimental, not an approved drug.
status
Someone proposed this peptide.
A researcher, an agent, or an algorithm wrote down the sequence and picked a target to hit.
A computer predicted how the peptide binds to its target.
An AI model like OpenFold3 or AlphaFold built a 3D structure and scored how well it fits the binding site.
Someone else ran the same prediction and got the same result.
A second contributor repeated the computation on their own hardware and the scores matched.
The peptide was actually made in a lab.
A chemistry service or a researcher ordered the sequence, it was manufactured, and mass spectrometry confirmed the right molecule was produced.
The peptide was tested on its target in a lab.
A binding or activity measurement confirmed that it actually does what the computer predicted — or didn't.
prediction metrics
ipTM0.750
pTM0.593
avg pLDDT90.8
ranking score0.877
STRUCTURE · PEP-04990 × ACE
ranking0.877
target interface 4.5Å peptide drag rotate · ctrl+scroll zoom · right-click pan
sequence
156
AAPLAP
details
▸full evidence table2 metrics
| metric | value | tool |
|---|---|---|
| ipTM | 0.7497579455375671 | boltz-2 |
| ranking score | 0.8767412304878235 | boltz-2 |
▸structural qualityopenfold3
| metric | value | note |
|---|---|---|
| gpde | 1.040 | global PDE — lower = better |
| disorder | NaN | fraction disordered |
▸3-letter notation
Ala-Ala-Pro-Leu-Ala-Pro
▸recipeboltz-2 1.0
| parameter | value |
|---|---|
| model | boltz-2 1.0 |
| weights | — |
| hardware | nvidia_nim_api |
| mlx version | — |
| python | — |
| random seed | — |
| msa strategy | none |
| diffusion samples | 1 |
| runtime | — |
| predicted by | mlx@peptide |
| predicted at | 2026-04-24 |
▸citationbibtex
peptidemodel (2026). Blood-pressure-lowering peptide (AAPLAP) (pep-04990, v1). PeptideModel. https://peptidemodel.com/card/pep-04990
@peptide{pep04990,
sequence = {AAPLAP},
target = {ace},
author = {peptidemodel},
year = {2026},
status = {computed}
} references
discussion
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