Equipment

A computer with internet access

Software

A web browser (the PRODIGY server has been tested successfully on Chrome, Firefox and Safari)
PRODIGY web server address: http://milou.science.uu.nl/services/PRODIGY
Software repositories for running a local version (not described in this protocol) under a Linux or MacOSX operating system:

PRODIGY repository (https://github.com/haddocking/binding_affinity)
freeSASA (http://freesasa.github.io)

Procedure

The software
1. Technical description
  PRODIGY is made freely available to the scientific community either as standalone software (https://github.com/haddocking/binding_affinity), which can be used locally on a desktop computer, or more conveniently as an online web-server, for which the usage is explained in this protocol. The PRODIGY software consists of a collection of Python scripts, a few Perl scripts to handle the online submission and the open-source tool freeSASA (Mitternacht, 2016) used to calculate the solvent accessible surface area, using default NACCESS (Hubbard and Thornton, 1993) parameters for atomic radii (http://freesasa.github.io).
2. Data requirement
How to use PRODIGY web-server
1. Submitting a prediction
  Here we describe the process of submitting a prediction run to the PRODIGY web-server (http://milou.science.uu.nl/services/PRODIGY). As example, we will use the protein-protein complex between an antibody (FAB) and HIV-1 capsid protein p24, that is present in the Protein Data Bank (PDB) with the access code ‘1E6J’.
2. The result page
  The result page is organized in three sections, reporting different information:
  1. Binding affinity and K_d prediction
    The name identifiers of your complex, which contains the PDB code of the retrieved file (or the name of the input you upload) is reported, together with the predicted ΔG (in kcal mol^-1) and K_d (in M) values at the given temperature. In this example, -9.1 kcal mol^-1 has been predicted for ΔG, corresponding to a K_d of 2.1e-07 M at 25 °C.
  2. Prediction details
    1. Number of ICs calculated within a threshold of 5.5 Å and % NIS classified according to the charged/polar/apolar character of the amino acids are reported. In this case, for example, there are 7 ICs between charged and polar residues and the % NIS charged atoms is 20.48.
    2. Further, the full table (format .txt) of ICs is provided and can be viewed by clicking on the link reported under ‘Table of the ICs at the interface’. The format of the table is the following:
      #chain1 #aa1 #res_num1 #chain2 #aa2 #res_num2
      H → THR →33PTHR210
      In which chain ID, residue type and residue number are reported for both residues interacting in Protein 1 and Protein 2.
  3. Download outputs
    In this foldable menu, it is possible to download a ready-to-run Pymol script (.pml) (http://www.pymol.org) that will highlight the interaction interface by displaying and coloring the interacting residues, see Figure 2. Further, it is possible to download a compressed file (.tgz) with all the result files.
    
    Figure 2. A three-dimensional representation of the complex 1E6J with the color-coding of the PRODIGY script (.pml). This script can be downloaded from the PRODIGY output page. Interactor 1 is shown in light pink (chains L and H in this example) and Interactor 2 in light blue (chain P), respectively. The interacting residues are represented in sticks in blue and dark pink for Interactor 1 and Interactor 2, respectively.
Useful information
1. Make sure to check and input the correct chain_IDs for the PDB file that you are uploading/retrieving: chain_IDs have to be present in the file, and correspond to the chains that are interacting. In this example, the FAB has two chains labeled as L and H, and both of them are interacting with the HIV1 capsid protein, which is labeled as chain P.
2. PRODIGY can deal with files consisting of an ensemble of structures (e.g., as is typical for NMR structures). In the current implementation, only the first model will be considered for prediction. If you wish to analyze every model present in such an ensemble you should split the PDB file into single-model PDB files and submit them all as an archive file. A collection of useful Python scripts for the manipulation of PDB files, such as splitting of ensemble file, residue renumbering, changing chain ID and so on, can be found in our freely available pdb-tools GitHub repository available online at https://github.com/haddocking/pdb-tools.
3. The PRODIGY web-server currently only supports the 20 canonical amino acids.
4. Information about the web-server input/output, the prediction method and its performance, and the dataset used for training/testing the method can be found online under the Manual/Method/Dataset PROGIDY pages respectively. These are reachable through the corresponding tabs located at the beginning of each page.
Distribution/Software download
The PRODIGY web-server is made freely available to the scientific community at: http://milou.science.uu.nl/services/PRODIGY. The prediction scripts are also available from our GitHub repository for local setup and usage at: https://github.com/haddocking/binding_affinity.
The collection of software developed by the HADDOCK group can be found at: http://www.bonvinlab.org/software.
The freeSASA software (Mitternacht, 2016) used to calculate the solvent accessible surface area can be downloaded from http://freesasa.github.io.

Notes

To run the ready-to-run Pymol script (.pml) provided by PRODIGY (see step B2c), open a Pymol session with the PDB code that you submitted to PRODIGY and follow one of the possible options:

From the bar menu of Pymol, choose File  Run and navigate in the directory where the PRODIGY Pymol script has been saved. Then select the .pml file clicking on ‘Open’.
In the Pymol terminal bar, type @ followed by the .pml file. Please note, if the Pymol session is not open in that folder, the user will need to type the full path. For example: @home/my_path/prodigy_pymol_script.pml

Acknowledgments

This protocol has been adapted from: Vangone and Bonvin (2015) and Xue et al. (2016). Anna Vangone was supported by H2020 Marie-Skłodowska-Curie Individual Fellowship MCSA-IF-2015 [BAP-659025].

References

Berman, H., Henrick, K. and Nakamura, H. (2003). Announcing the worldwide Protein Data Bank. Nat Struct Biol 10(12): 980.
Hubbard, S. J. and Thornton, J. M. (1993). Naccess. Computer Program.
Kastritis, P. L. and Bonvin, A. M. (2010). Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. J Proteome Res 9(5): 2216-2225.
Kastritis, P. L., Moal, I. H., Hwang, H., Weng, Z., Bates, P. A., Bonvin, A. M. and Janin, J. (2011). A structure-based benchmark for protein-protein binding affinity. Protein Sci 20(3): 482-491.
Mitternacht, S. (2016). FreeSASA: An open source C library for solvent accessible surface area calculations. F1000Res 5: 189.
Vangone, A., and Bonvin, A. M. J. J. (2015). Contacts-based prediction of binding affinity in protein-protein complexes. eLife 4: 291.
Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jimenez-Garcia, B., Bates, P. A., Fernandez-Recio, J., Bonvin, A. M. and Weng, Z. (2015). Updates to the integrated protein-protein interaction benchmarks: Docking benchmark version 5 and affinity benchmark version 2. J Mol Biol 427(19): 3031-3041.
Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M. and Vangone, A. (2016). PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics.