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PRODIGY (PROtein binDIng enerGY prediction) is a collection of web services focused on the prediction of binding affinity in biological complexes as well as the identification of biological interfaces from crystallographic one.

  • To predict the binding affinity in PROTEIN-PROTEIN complexes, please visit the PRODIGY tab.
  • To predict the binding affinity in PROTEIN-SMALL LIGAND complexes, please visit the PRODIGY-LIG tab.
  • To classify interfaces between biological or crystallographic, please visit the PRODIGY-CRYSTAL tab.

To use any of the PRODIGY tools, you just need to provide the 3D structure of your complex/complexes in PDB/mmCIF format or the ID of its PDB entry. Depending on the input data to be used, your request is expected to take a few seconds before you receive the results. If an e-mail address is provided, you will be notified when your job has finished.

Number of successfully served jobs:

  • PRODIGY (since July 2016): 253479
  • PRODIGY-LIGAND (since April 2018): 27710
  • PRODIGY-CRYSTAL (since November 2018): 4104

User satisfaction rating on a scale from 1 to 5 (best):

PRODIGY-Webinar: learn more about PRODIGY in the 7th BioExcel webinar. YouTube

Run PRODIGY

Please provide the PDB ID of the target complex or submit a file in PDB or mmCIF format.

An archive of multiple PDB/mmCIF files can also be provided (as a multi-model PDB), together with the interacting chains.

Information about the PRODIGY-CRYSTAL predictive approach can be found at the online method page.

OR
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Add a custom Job ID to identify your run.
Optional email address to which the results will be send.

Once you click on the button, your job will be processed. If you have provided an email address, the link to the output page will be emailed to you.

The electrostatic interaction energy calculated by the HADDOCK refinement page. When submitting a PDB file obtained directly from the HADDOCK refinement page, this value will be retrieved automatically.
If no electrostatic energy is provided, the server will run the "No electrostatics prediction" protocol.
Further information is provided in the manual page.
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This work is co-funded by the Horizon 2020 projects EOSC-hub and EGI-ACE (grant numbers 777536 and 101017567), BioExcel (grant numbers 823830 and 675728)
and by a computing grant from NWO-ENW (project number 2019.053).