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      Fpocket: An open source platform for ligand pocket detection

      product-review
      1 , 2 , 3 , 4 ,
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.

          Results

          Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster.

          Conclusion

          Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at http://fpocket.sourceforge.net.

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          Most cited references32

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          CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues

          Cavities on a proteins surface as well as specific amino acid positioning within it create the physicochemical properties needed for a protein to perform its function. CASTp () is an online tool that locates and measures pockets and voids on 3D protein structures. This new version of CASTp includes annotated functional information of specific residues on the protein structure. The annotations are derived from the Protein Data Bank (PDB), Swiss-Prot, as well as Online Mendelian Inheritance in Man (OMIM), the latter contains information on the variant single nucleotide polymorphisms (SNPs) that are known to cause disease. These annotated residues are mapped to surface pockets, interior voids or other regions of the PDB structures. We use a semi-global pair-wise sequence alignment method to obtain sequence mapping between entries in Swiss-Prot, OMIM and entries in PDB. The updated CASTp web server can be used to study surface features, functional regions and specific roles of key residues of proteins.
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            Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites.

            Identifying the location of ligand binding sites on a protein is of fundamental importance for a range of applications including molecular docking, de novo drug design and structural identification and comparison of functional sites. Here, we describe a new method of ligand binding site prediction called Q-SiteFinder. It uses the interaction energy between the protein and a simple van der Waals probe to locate energetically favourable binding sites. Energetically favourable probe sites are clustered according to their spatial proximity and clusters are then ranked according to the sum of interaction energies for sites within each cluster. There is at least one successful prediction in the top three predicted sites in 90% of proteins tested when using Q-SiteFinder. This success rate is higher than that of a commonly used pocket detection algorithm (Pocket-Finder) which uses geometric criteria. Additionally, Q-SiteFinder is twice as effective as Pocket-Finder in generating predicted sites that map accurately onto ligand coordinates. It also generates predicted sites with the lowest average volumes of the methods examined in this study. Unlike pocket detection, the volumes of the predicted sites appear to show relatively low dependence on protein volume and are similar in volume to the ligands they contain. Restricting the size of the pocket is important for reducing the search space required for docking and de novo drug design or site comparison. The method can be applied in structural genomics studies where protein binding sites remain uncharacterized since the 86% success rate for unbound proteins appears to be only slightly lower than that of ligand-bound proteins. Both Q-SiteFinder and Pocket-Finder have been made available online at http://www.bioinformatics.leeds.ac.uk/qsitefinder and http://www.bioinformatics.leeds.ac.uk/pocketfinder
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              Detection, delineation, measurement and display of cavities in macromolecular structures.

              A computer program, VOIDOO, is described which can be employed in the study of cavities such as they occur in macromolecular structures (in particular, in proteins). The program can be used to detect unknown cavities or to delineate known cavities, either of which may be connected to the outside of the molecule or molecular assembly under study. Optionally, output files can be requested that contain a description of the shape of the cavity which can be displayed by the crystallographic modelling program O. Additionally, VOIDOO can be used to calculate the volume of a molecule and to create a file containing data pertaining to the surface of the molecule which can also be displayed using O. Examples of the use of VOIDOO are given for P2 myelin protein, cellular retinol-binding protein and cellobiohydrolase II. Finally, operational definitions to discern different types of cavity are introduced and guidelines for assessing the accuracy and improving the comparability of cavity calculations are given.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2009
                2 June 2009
                : 10
                : 168
                Affiliations
                [1 ]ICOA – Institut de chimie organique et analytique – UMR CNRS 6005, Div. of chemoinformatics and molecular modeling, University of Orléans, Orléans, France
                [2 ]Dpto Fisicoquimica, Fac Farmacia, Univ Barcelona, Barcelona, Spain
                [3 ]Molécules Therapeutiques in silico, INSERM, UMR-S 973, University Paris Diderot – Paris 7, Paris, France
                [4 ]Ressource Parisienne en Bioinformatique Structurale, University Paris-Diderot, Paris, France
                Article
                1471-2105-10-168
                10.1186/1471-2105-10-168
                2700099
                19486540
                e05285de-2f1d-432d-8477-22549fd38b78
                Copyright © 2009 Le Guilloux et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 March 2009
                : 2 June 2009
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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