Abstract
BackgroundProtein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking – how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.ResultsWe have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms.ConclusionsThe positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at http://siret.ms.mff.cuni.cz/prank.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0059-5) contains supplementary material, which is available to authorized users.
Highlights
Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function
Accurate prediction of ligand-binding sites, often called pockets, from a 3D protein structure plays a pivotal role in rational drug design [1,2] and can be helpful in drug side-effects prediction [3] and elucidation of protein function [4]
Not all reported pockets usually correspond to true binding sites, but it is expected that entries at the top of the ordered list correspond to regions with the highest probability of being a true binding site. It is not unusual for one protein to have more than one ligand-binding site, the Krivák and Hoksza Journal of Cheminformatics (2015) 7:12 number of putative pockets predicted by pocket detection methods tends to be much higher than the number of actual known positives
Summary
Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Empirical studies show that the actual ligand-binding sites tend to coincide with the largest and deepest pocket on Plethora of pocket detection methods, that employ variety of different strategies, are currently available These include purely geometric methods, energetic methods and methods that make use of evolutionary conservation (see below). Not all reported pockets usually correspond to true binding sites, but it is expected that entries at the top of the ordered list correspond to regions with the highest probability of being a true binding site It is not unusual for one protein to have more than one ligand-binding site, the Krivák and Hoksza Journal of Cheminformatics (2015) 7:12 number of putative pockets predicted by pocket detection methods tends to be much higher than the number of actual known positives. The accuracy of a pocket prediction method is evaluated by its ability to yield the true (experimentally confirmed) binding sites among the top-n putative pockets on its output (where n is usually taken to be 1, 3 or 5)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
