Jendele L, Krivak R, Skoda P, Novotny M, Hoksza D. Web-server article in NAR about the web interface accessible at.P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Software article in JChem about P2Rank pocket prediction tool.If you use P2Rank, please cite relevant papers: Presentation slides introducing the original version of the algorithm: Slides (pdf) Publications Ligandability score of individual points is determined by a machine learning based model trained on the dataset of known protein-ligand complexes.įor more details see the slides and publications.
P2Rank makes predictions by scoring and clustering points on the protein's solvent accessible surface. Usage prank predict -f test_data/1fbl.pdb # predict pockets on a single pdb file Binary packages are available as GitHub Releases. On Windows, it is recommended to use the bash console to execute the program instead of cmd or PowerShell.
P2Rank is tested on Linux, macOS, and Windows. It achieves high prediction success rates without relying on an external software for computation of complex features or on a database of known protein-ligand templates. P2Rank is a stand-alone command line program that predicts ligand-binding pockets from a protein structure. Ligand-binding site prediction based on machine learning.
