FAQ and Help
pyDockEneRes server uses three different sources of input data:
- User custom PBD structures. This option is identified by the label 'Upload a Complex PDB structure'. When this option is selected, the user is asked to upload a PDB file, containing a protein complex.
- RCSB code. This option is identified by the label 'Select PDB by RCSB code'. When the user selects this option, pyDockEneRes polls the RCSB Protein Data Bank and download the PDB structure automatically (only if exists).
- Compressed job file. This is the option for uploading large amount of data structures to be calculated by the pyDockEneRes server.
Two different compression formats are accepted for this file: .tgz and .zip.
The compressed job files must contain a job.csv file and a set of different PDB files, two per complex (one for receptor and one for ligand).
For each complex to be calculated, one line of the format 'pdb_file_1.pdb:A,B:pdb_file_2.pdb:A,C' is included in the job.csv file,
where pdb_file_1.pdb is the PDB structure file for the complex receptor, pdb_file_2.pdb for the complex ligand and A,B are the chains to be used from the receptor PDB file and A,C
the chains to be used from the ligand PDB file.
Note that job.csv file cannot contain any folder or subfolders. When you compress your data, make sure you do it as follows:
tar my_data.tgz job.csv *.pdb
job.csv file example:
2OOB_rec.pdb:A:2OOB_lig.pdb:B 2UUY_rec.pdb:A:2UUY_lig.pdb:B 1HIA_rec.pdb:A,B:1HIA_lig.pdb:CDownload a working compressed job file as a example.
PDB structure files
In order to avoid invalid results, you are encouraged to follow the PDB file format. Note that incomplete backbone or side-chain atoms may incur in inaccurate results and/or software failure.
pyDockEneRes server provides several data after the job has finished. File names containing rec represent some output calculated on the receptor partner. The same applies for the ligand with lig.
- Extension .ligand.ALAddg. The file contains for each of the residues of the subunit, receptor or ligand, the value for the pyDock side-chain energetic terms (eleSC, solvSC, vdwSC), and the estimated ΔΔG upon mutation to Alanine (ddg_ala). For example:
conf subunit residue eleSC solvSC vdwSC ddg_ala 1 ligand B.PRO.233 -0.02359 -0.00000 -0.00278 0.02637 1 ligand B.LYS.234 0.14505 -0.00001 -0.07433 -0.07073 1 ligand B.PHE.235 0.00640 0.00000 -0.09980 0.09340 1 ligand B.THR.236 0.04144 0.00000 -0.00593 -0.03551 1 ligand B.LYS.237 -0.26721 0.00000 -0.04025 0.30745
- Extension .receptor.residueEne. The file contains for each of the residues of the subunit, receptor or ligand, the value for the pyDock energetic terms (ele, solv and vdw) and pyDock total energy (ele+solv+0.1vdw). For example:
conf subunit residue ele solv vdw total 1 receptor E.ILE.16 0.85841 0.00000 -0.17422 0.84099 1 receptor E.VAL.17 0.00090 0.00000 -0.14643 -0.01374 1 receptor E.GLY.18 -0.02346 0.00000 -0.01421 -0.02488 1 receptor E.GLY.19 0.01724 0.00000 -0.00919 0.01632 1 receptor E.TYR.20 -0.00832 0.00000 -0.01081 -0.00940
- Extension pdb.1.residueEne. The file contains the atomic data in PDB format of the subunit, including the value of the residue pyDock total energy in the b-factor column. For example:
ATOM 860 N GLY A 104 44.018 13.058 130.215 1.00 -0.00 ATOM 861 CA GLY A 104 45.280 12.901 129.530 1.00 -0.00 ATOM 862 C GLY A 104 46.411 12.586 130.478 1.00 -0.00 ATOM 863 O GLY A 104 47.495 12.207 130.044 1.00 -0.00 ATOM 864 N ALA A 105 46.154 12.707 131.775 1.00 -0.00
- Extension .pdb.H. This file contains the PDB structure parsed by pyDock, including hydrogens.
- Extension .amber. Internally, pyDock makes use of AMBER94 force-field charges. This file includes the different energetic terms by atom used by pyDock
The information contained in the above result files is provided for sake of reproducibility and in-deep detailed information for pyDock advanced users.
To cite pyDockEneRes, please reference:
Romero-Durana M., Jiménez-García B., Fernández-Recio J. (2020) pyDockEneRes: per-residue decomposition of protein–protein docking energy, Bioinformatics, 36, 2284–2285. DOI: https://doi.org/10.1093/bioinformatics/btz884