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P2Rank

Type: 
Bioinformatics & Cheminformatics
One line description: 
Protein-Ligand binding site prediction
Annotation: 
 
P2Rank is a machine learning based method for prediction of ligand binding sites from protein structure. P2Rank uses Random Forests classifier to infer ligandability of local chemical neighborhoods near the protein surface which are represented by specific near-surface points and described by aggregating physico-chemical features projected on those points from neighboring protein atoms. The points with high predicted ligandability are clustered and ranked to obtain the resulting list of binding site predictions. P2Rank is freely available at github.com/rdk/p2rank .
 
 
Developers: 
david.hoksza
radoslav.krivak

 



Introduction

 
P2Rank is a stand-alone command line program that predicts ligand binding pockets from a protein structure. 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 makes predictions by scoring and clustering points on the protein's solvent accessible surface. Ligandability score of individual points is determined by a machine learning based model trained on the dataset of known protein-ligand complexes. For more details see slides and publications.

Slides with method overview: p2rank_slides
 

Download
 

p2rank_2.0.1 (109MB)
(stand-alone, platform independent binary package, requires Java 1.8 or newer)

Check out GitHub release page for the latest development releases.
 

Source 


Source code and documentation is available on GitHub: github.com/rdk/p2rank
 

Web Version


Web interface for the method is called PrankWeb and is now available at prankweb.cz
 

PDBe-KB


Bidning site predictions for large part of PDB are now available via PDBe Knowledge Base at www.ebi.ac.uk/pdbe/pdbe-kb . You can find P2Rank predictions among Functional anotations / Predicted ligend binding sites, see for example www.ebi.ac.uk/pdbe/pdbe-kb/proteins/2etx#annotations .

 

Publications


If you use P2Rank, please cite relevant papers:

  • Software article in JChem about P2Rank pocket prediction tool: Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Journal of Cheminformatics. 2018 Aug.
  • Conference paper inroducing P2Rank prediction algorithm: Krivák R, Hoksza D. P2RANK: Knowledge-Based Ligand Binding Site Prediction Using Aggregated Local Features. International Conference on Algorithms for Computational Biology 2015 Aug 4 (pp. 41-52). Springer, Cham.
  • Research article in JChem about PRANK rescoring algorithm: Krivák R, Hoksza D. Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features. Journal of Cheminformatics. 2015 Dec;7(1):12.
  • Web-server article in NAR: Jendele L, Krivak R, Skoda P, Novotny M, Hoksza D. PrankWeb: a web server for ligand binding site prediction and visualization. Nucleic Acids Research, Volume 47, Issue W1, 02 July 2019, Pages W345–W349
     

Feedback


We would be happy to hear about your use cases, experiences and ideas/feature requests. Either raise an issue on GitHub issue tracker or get in touch by mail. Please address any correspondence to both (rkrivak [at] gmail.com) and (david.hoksza [at] gmail.com).