Dataset and evalutaion tools of the Shrec 2022 contest on protein-ligand binding site recognition
The script has been tested on Ubuntu 20.04.4 LTS and macOS Catalina 10.15.7.
Python3 must be installed
- numpy
If not installed: pip3 install numpy
(recommended for OS)
Move in the installLIB folder and type ./install_script. This will compile the shared C library and move it in the main folder under the name: libCfunc.so
For evaluating putative pockets in PQR or as a boolean map for the vertices of the structure OFF format (TXT file):
If OFF format, in the working directory the "testData" folder must be present. This folder contains the structures in OFF format. It can be downloaded from https://github.com/concept-lab/shrec22_proteinLigandBenchmark.git
python3 evaluate.py <directoryName containing participant results>
Change in the script the fields pCoverageTH = 0.2 lCoverageTH = 0.5 to change the metric's threshold for the evaluation of a putative binding site(*).
Participant results are in the participantResults folder
rankStats.txt: file containing the ranking result (Top1, Top3, Top10 and metrics--LC and PC score as described in*)
failureList.txt: file containing the list of structure-ligands pairs not matched by any of the putative pockets.
The script filterLig.py, creates a folder containing all ligands (in xyz format) keeping only ligand atoms within 5A from any correspondent protein atom. These are actually the ligand coordinates used for evaluation (the same filtering process is performed within evaluate.py by providing the structures pqrs contained in the "allStructures" folder).
A lighter version (without the full database and PQR structure files)of the contest's participants evaluation tool is provided in https://github.com/concept-lab/shrec22_PLBinding_evaluationTools.git
https://arxiv.org/pdf/2206.06035.pdf
(*) L. Gagliardi et al, SHREC 2022: Protein-ligand binding site recognition, Computers & Graphics https://doi.org/10.1016/j.cag.2022.07.005 (2022)