Cross-Beta predictor - Amyloidogenicity predictor based on a machine learning approach

The model has been trained on a positive set including amino acid sequences derived from naturally-occurring cross-β amyloids from the Cross-Beta DB and a negative dataset containing Intrinsically Disordered Regions (IDRs) known to be soluble.

Citing Cross-Beta RF predictor:
Valentin Gonay, Michael P. Dunne, Javier Caceres-Delpiano, & Andrey V. Kajava. (2024). Developing machine-learning-based amyloid predictors with Cross-Beta DB. bioRxiv, 2024.02.12.579644. https://doi.org/10.1101/2024.02.12.579644

Contact : valentin.gonay@crbm.cnrs.fr

Paste one protein sequence (Example):

NOTE : Maximum 1000 characters

Options




Option details:
Threshold: All scores above this value will be considered positive (amyloid).

Window size: This value can either be "auto" or must be equal to or less than the length of the query sequence. The "auto" value will take a window of size = 10% of the sequence length with a minimum of 15 and a maximum of 50.

In association with: