PrankWeb: a web server for ligand binding site prediction and visualization

PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

PrankWeb: web server for ligand binding-site prediction and visualization

ABSTRACTPrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art ligand binding site prediction method. P2Rank is a template-free machine learning method which is based on the prediction of ligandability of local chemical neighborhoods centered on points placed on a solvent accessible surface of a protein. Points with high ligandability score are then clustered to form the resulting ligand binding sites. On top of that, PrankWeb then provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use it in both the prediction and results visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility to export the results as a PyMOL script for offline visualization. The web frontend communicates with the serer side via a REST API. Therefore, in high-throughput scenarios users can utilize the server API directly, bypassing the need for a webbased front end or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/. The source code of the web application and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

Identification of protein pockets and cavities by Euclidean Distance Transform

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This research presents PoCavEDT, an automated purely geometric technique for the identification of binding pockets and occluded cavities in proteins based on the 3D Euclidean Distance Transform. Candidate protein pocket regions are identified between two Solvent-Excluded surfaces generated with the Euclidean Distance Transform using different probe spheres, which depend on the size of the binding ligand. The application of simple, yet effective geometrical heuristics ensures that the proposed method obtains very good ligand binding site prediction results. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities. Its performance was compared to the results achieved with several purely geometric pocket and cavity prediction methods, namely SURFNET, PASS, CAST, LIGSITE, LIGSITECS, PocketPicker and POCASA. Success rates PoCavEDT were comparable to those of POCASA and outperformed the other software.

Identification of protein pockets and cavities by Euclidean Distance Transform

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This research presents PoCavEDT, an automated purely geometric technique for the identification of binding pockets and occluded cavities in proteins based on the 3D Euclidean Distance Transform. Candidate protein pocket regions are identified between two Solvent-Excluded surfaces generated with the Euclidean Distance Transform using different probe spheres, which depend on the size of the binding ligand. The application of simple, yet effective geometrical heuristics ensures that the proposed method obtains very good ligand binding site prediction results. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities. Its performance was compared to the results achieved with several purely geometric pocket and cavity prediction methods, namely SURFNET, PASS, CAST, LIGSITE, LIGSITECS, PocketPicker and POCASA. Success rates PoCavEDT were comparable to those of POCASA and outperformed the other software.