Use PRODIGY (PROtein binDIng enerGY) Online
Commercially Available PRODIGY (PROtein binDIng enerGY) No-Code Web Server
PRODIGY: Predicting Protein-Protein Binding Affinity from 3D Structure
PRODIGY (PROtein binDIng enerGY prediction) is a structure-based predictor of the binding affinity of protein-protein complexes. It was developed by Li C. Xue, Joao P. G. L. M. Rodrigues, Panagiotis L. Kastritis, Alexandre M. J. J. Bonvin and Anna Vangone, and described as a web server in Bioinformatics in 2016. The authors also describe a standalone local version of the software.
Binding affinity tells you how tightly two proteins associate, which matters for understanding signaling, designing biologics and ranking docking models. Measuring it experimentally is slow, so a fast prediction from a 3D structure is valuable.
PRODIGY uses a contact-based model plus properties of the non-interacting surface to turn a structure into a predicted free energy and dissociation constant in seconds.
Attribute | Detail |
|---|---|
Input | 3D structure of a complex (PDB or mmCIF), or an archive for batch analysis |
Predicted outputs | Binding free energy (delta G) and dissociation constant (Kd) |
Interface descriptor | Intermolecular contacts within a 5.5 angstrom cutoff |
Default temperature | 25 degrees C |
Reported accuracy | Pearson correlation 0.73, RMSE 1.89 kcal/mol |
How PRODIGY Predicts Protein-Protein Binding Affinity
Count interface contacts: the model counts intermolecular contacts between residues of the two partners using a 5.5 angstrom distance cutoff.
Characterize the non-interacting surface: it adds the percentages of charged and polar amino acids on the surface that does not take part in the interface.
Compute solvent accessibility: solvent accessible surface area is calculated with freeSASA using default NACCESS atomic radii.
Predict energy and Kd: the free energy is converted to a dissociation constant through delta G = RT ln(Kd) at the chosen temperature.
PRODIGY Accuracy on the Protein-Protein Binding Affinity Benchmark
The model was trained and evaluated on the protein-protein binding affinity benchmark of Kastritis and colleagues (2011).
Metric | Reported value |
|---|---|
Pearson correlation (predicted vs measured) | 0.73 (P below 0.0001) |
Root mean square error | 1.89 kcal/mol |
Processing time for the largest benchmark complex (1DE4) | A few seconds |
Outputs
The server reports delta G, Kd, counts of contacts by type, the composition of the non-interacting surface, an interface residue table, and a PyMOL script for visualizing the interface.
What is Tamarind Bio?
Tamarind Bio is a no-code bioinformatics platform built to give life scientists and researchers access to powerful computational tools. Many cutting-edge machine learning models are hard to deploy and use. Tamarind provides an intuitive, web-based environment that removes the complexity of high-performance computing, software dependencies and command-line interfaces.
The platform is designed for biologists, chemists and other researchers who may not have a background in programming or cloud infrastructure but want to run models on their own data. Key features include:
A user-friendly graphical interface for setting up and launching experiments
A robust API for integration into existing research pipelines
An automated system for managing and scaling computational resources
Tamarind treats information and data security as a top priority, as detailed in its Trust Center and Terms of Service.
Accelerating Discovery with PRODIGY on Tamarind Bio
Ranking docking models: estimate affinity for candidate poses of a protein-protein complex.
Comparing mutants or designs: compare predicted affinities across variants of an interface.
Batch screening: analyze many complexes at once, as the server supports archive uploads.
Interface analysis: inspect contact types and interface residues alongside the predicted energy.
How to Use PRODIGY on Tamarind Bio
Log in and open the tool: sign in at tamarind.bio and select PRODIGY.
Provide the complex: upload a structure in PDB or mmCIF format, as the source defines.
Define the partners: specify which chains belong to each interacting partner, if exposed.
Set parameters: adjust the temperature if exposed; the default in the paper is 25 degrees C.
Run the job: launch it and wait for the results, which take seconds for typical complexes.
Download outputs: retrieve delta G, Kd, contact counts and the interface residue table.
Visualize: use the PyMOL script, if exposed, to view interface residues.
Use results downstream: rank designs or docking poses and pick candidates for further modeling.
Things to Keep in Mind
Some natural ultra-high-affinity complexes have average or below-average buried surface area, so the model may underestimate their affinity.
For NMR ensembles submitted as a single PDB file, only the first model is used.
Predictions are for structures of complexes, so the quality of the input structure matters.
The authors note that no alternative physical model they tested performed better on average than the contact-based approach.
Source: Li C. Xue, Joao P. G. L. M. Rodrigues, Panagiotis L. Kastritis, Alexandre M. J. J. Bonvin and Anna Vangone, PRODIGY: a web server for predicting the binding affinity of protein-protein complexes, Bioinformatics 32(23):3676-3678 (2016). DOI 10.1093/bioinformatics/btw514. Paper: https://academic.oup.com/bioinformatics/article/32/23/3676/2525629. Web server: http://milou.science.uu.nl/services/PRODIGY