UdonPred
Residue-level intrinsic disorder scoring using TriZOD outputs.
Summary
- Model name:
UdonPred - Task: Intrinsic disorder prediction
- Protocol:
residue_to_value - Embedder:
Rostlab/ProstT5 - Model size:
1.1 MB
Usage
Using the biocentral Python API:
from biocentral_api import BiocentralAPI, BiocentralPredictionModel
biocentral_api = BiocentralAPI().wait_until_healthy(max_wait_seconds=10)
model_names = [BiocentralPredictionModel.UDONPRED]
sequence_data = {
"Seq1": "MMALSLALMM",
"Seq2": "PRTEINMMALM",
"Seq3": "PRTSSSLAMAM",
"Seq4": "SEQWENCEAWMMWW",
}
result = biocentral_api.predict(model_names=model_names, sequence_data=sequence_data).run()
print(result)Using the biocentral IRE:
Outputs
disorder_trizod
- Description: Disorder scores (TriZOD)
- Output type:
per_residue - Value type:
float
References
- Authors: Julius Schlensok; David Wagemann; Tobias Senoner; Markus Haak; Burkhard Rost
- Model link: github.com/DavidWagemann/UdonPred
- Citation: doi.org/10.64898/2026.01.26.701679
- License:
GPL-3.0 - Training data: figshare.com/articles/dataset/UdonPred/31444642