Abstract
BackgroundLigand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers.ResultsWe present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.ConclusionsP2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.
Highlights
Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery
Since we wanted to compare the viability of the methods, not just robustness of their implementations, we considered success rates only on subsets of original datasets on which given tools finished successfully and produced predictions
Predictive model of DeepSite is deep convolutional neural network trained on a large dataset of 7622 structures derived from sc-the Protein Data Bank (PDB) [65] database
Summary
Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. Motivation Prediction of ligand binding sites (LBS, or pockets1) from protein structure has many applications in elucidation of protein function [1] and rational drug design [2,3,4]. It has been employed in drug side-effects prediction [5], fragment-based drug discovery [6], docking prioritization [7, 8], structure based virtual screening [9] and structure-based target prediction (or so called inverse virtual screening) [10]. Allosteric site prediction tools Allosite [17] and AlloPred [17] both employ pocket prediction tool Fpocket [18] as the first step of their algorithms
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