Welcome to MEDUSA webserver

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Multiclass flexibility prediction from sequences of amino acids

MEDUSA is a Deep Learning approach for prediction of protein flexibility from sequence. MEDUSA takes as input an amino acid sequence and returns a flexibility class of each residue in terms of the expected normalized B-factor value range. Prediction is simultaneously performed in two-, three- and five classes using a convolutional neural network trained on a dataset of non-redundant X-ray structures.


See an example of the webserver's output here.

Please use the following reference when citing the MEDUSA webserver:

Vander Meersche, Y., Cretin, G., de Brevern, A. G., Gelly, J. C., & Galochkina, T. (2021). MEDUSA: Prediction of protein flexibility from sequence. Journal of Molecular Biology, 166882. https://doi.org/10.1016/j.jmb.2021.166882

For now, the webserver works for single sequence prediction only. To launch batches of predictions please use the standalone program (Docker container) freely available on github. We provide a simple example of few bash lines to run MEDUSA on multiple sequences (multifasta).
Input sequence
Paste sequence example]:

Or, browse a sequence file:
Job options
The database used for HHBlits search is now: UniRef30_2021_03 (Previously UniRef30_2020_03)
The one used in the reference paper of MEDUSA was UniRef30_2016_09
This web server is for an academic non-commercial use only, for an other use please contact us.
Using MEDUSA web server means that you have read and you agree to comply with the following terms of use.
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