MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets

Bioinformatics, Feb 2011

Motivation: Identification of ligand binding pockets on proteins is crucial for the characterization of protein functions. It provides valuable information for protein–ligand docking and rational engineering of small molecules that regulate protein functions. A major number of current prediction algorithms of ligand binding pockets are based on cubic grid representation of proteins and, thus, the results are often protein orientation dependent. Results: We present the MSPocket program for detecting pockets on the solvent excluded surface of proteins. The core algorithm of the MSPocket approach does not use any cubic grid system to represent proteins and is therefore independent of protein orientations. We demonstrate that MSPocket is able to achieve an accuracy of 75% in predicting ligand binding pockets on a test dataset used for evaluating several existing methods. The accuracy is 92% if the top three predictions are considered. Comparison to one of the recently published best performing methods shows that MSPocket reaches similar performance with the additional feature of being protein orientation independent. Interestingly, some of the predictions are different, meaning that the two methods can be considered complementary and combined to achieve better prediction accuracy. MSPocket also provides a graphical user interface for interactive investigation of the predicted ligand binding pockets. In addition, we show that overlap criterion is a better strategy for the evaluation of predicted ligand binding pockets than the single point distance criterion. Availability: The MSPocket source code can be downloaded from http://appserver.biotec.tu-dresden.de/MSPocket/. MSPocket is also available as a PyMOL plugin with a graphical user interface. Contact: hongboz{at}biotec.tu-dresden.de; mayte{at}biotec.tu-dresden.de Supplementary information: Supplementary data are available at Bioinformatics online.

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MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets

Hongbo Zhu 0 M. Teresa Pisabarro 0 Associate Editor: Burkhard Rost 0 Structural Bioinformatics, BIOTEC Technical University of Dresden , Tatzberg 47-51, 01307 Dresden, Germany Motivation: Identification of ligand binding pockets on proteins is crucial for the characterization of protein functions. It provides valuable information for protein-ligand docking and rational engineering of small molecules that regulate protein functions. A major number of current prediction algorithms of ligand binding pockets are based on cubic grid representation of proteins and, thus, the results are often protein orientation dependent. Results: We present the MSPocket program for detecting pockets on the solvent excluded surface of proteins. The core algorithm of the MSPocket approach does not use any cubic grid system to represent proteins and is therefore independent of protein orientations. We demonstrate that MSPocket is able to achieve an accuracy of 75% in predicting ligand binding pockets on a test dataset used for evaluating several existing methods. The accuracy is 92% if the top three predictions are considered. Comparison to one of the recently published best performing methods shows that MSPocket reaches similar performance with the additional feature of being protein orientation independent. Interestingly, some of the predictions are different, meaning that the two methods can be considered complementary and combined to achieve better prediction accuracy. MSPocket also provides a graphical user interface for interactive investigation of the predicted ligand binding pockets. In addition, we show that overlap criterion is a better strategy for the evaluation of predicted ligand binding pockets than the single point distance criterion. Availability: The MSPocket source code can be downloaded from http://appserver.biotec.tu-dresden.de/MSPocket/. MSPocket is also available as a PyMOL plugin with a graphical user interface. Contact: ; Supplementary information: Supplementary data are available at Bioinformatics online. The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: 1 INTRODUCTION The prediction of ligand binding sites on proteins provides important information for proteinligand docking and structuralbased rational engineering of small molecules that modulate protein functions (Campbell et al., 2003; Sotriffer and Klebe, 2002). Furthermore, comparative analysis of ligand binding pockets is found to provide valuable information for the understanding of proteinligand binding specificity (Chen and Honig, 2010). It has been observed that ligand binding sites often locate in the largest pockets on protein surfaces (London et al., 2010; Nayal and Honig, 2006). Thus, the identification of pockets on protein surfaces plays a key role in the prediction of protein functional sites, in particular, ligand binding sites. A variety of computational approaches have been proposed for the prediction of ligand binding pockets. These methods can be divided into two categories according to the information they utilize to detect pockets: geometric approaches that are purely based on the geometric characteristics of proteins, and comprehensive approaches that not only consider geometric criteria but also take into account evolutionary information, interaction energy or chemical properties of proteins. A major number of these methods, in both categories, are based on the cubic grid representation of protein structures. Geometric methods like POCKET (Levitt and Banaszak, 1992), LIGSITE (Hendlich et al., 1997) and LIGSITEcs (Huang and Schroeder, 2006) generate 3D grids for proteins and identify surface pockets as the set of solvent grid points that are situated between protein grid points. PocketPicker (Weisel et al., 2007) uses grids to represent proteins and search the environment of each surface grid along 30 directions for defining pockets. Tripathi and Kellogg (2010) introduced the VICE program as part of the HINT toolkit (Kellogg et al., 2005). Similar to PocketPicker, VICE scans grid points along the path in various directions at each grid points and defines pocket grids as those with at least half of the scan directions blocked. The VICE program represents proteins as binary grid maps, in which grid points occupied by atoms are set to one and the rest zero, such that the VICE algorithm is performed on only integers and thus is very efficient. Yu et al. (2010) suggested the Roll algorithm, in which a probe sphere of radius 2 is used to roll on each slice of the 3D grid representations of proteins. Pockets are defined to be the regions between the probe sphere and the protein surface. The grid representation of proteins is dependent on the orientation of proteins in the coordinate system. Inconsistent results may be observed for grid-based methods if the atomic coordinates of proteins are transformed. One solution to address the problem of inconsistent results is to incr (...truncated)


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Hongbo Zhu, M. Teresa Pisabarro. MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets, Bioinformatics, 2011, pp. 351-358, 27/3, DOI: 10.1093/bioinformatics/btq672