Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes*

By Jianghong An, Maxim Totrov, Ruben Abagyan. Molecular & Cellular Proteomics, 2005

395 citations recorded by the source corpus.

We developed a new computational algorithm for the accurate identification of ligand binding envelopes rather than surface binding sites. We performed a large scale classification of the identified envelopes according to their shape and physicochemical properties. The predicting algorithm, called PocketFinder, uses a transformation of the Lennard-Jones potential calculated from a three-dimensional protein structure and does not require any knowledge about a potential ligand molecule. We validated this algorithm using two systematically collected data sets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank, 5616 and 11,510, respectively. As many as 96.8% of experimental binding sites were predicted at better than 50% overlap level. Furthermore 95.0% of the asserted sites from the apo receptors were predicted at the same level. We demonstrate that conformational differences between the apo and bound pockets do not dramatically affect the prediction results. The algorithm can be used to predict ligand binding pockets of uncharacterized protein structures, suggest new allosteric pockets, evaluate feasibility of protein-protein interaction inhibition, and prioritize molecular targets. Finally the data base of the known and predicted binding pockets for the human proteome structures, the human pocketome, was collected and classified. The pocketome can be used for rapid evaluation of possible binding partners of a given chemical compound.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9685df3fe9b76820df2d5d6540457aeb989f3204

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Citations in the corpus

Fpocket: An open source platform for ligand pocket detection (2009, methodology): “Roughly, some are based on pure geometric analysis of the surface of the protein [4-15, 18 , 20, 22-26 22-26 ], whereas some others involve energy calculations [ 16 , 17].”
Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, background): “In the first approach, the ligand-binding pocket is identified by recognizing the surface cavities on the 3D structural model of the target protein (An et al., 2005; Capra et al., 2009; Hendlich et al., 1997; Laskowski, 1995).”
PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, neutral): “, SiteHound [10], Q-sitefinder [10], PocketFinder [1]).”
EADock: Docking of small molecules into protein active sites with a multiobjective evolutionary optimization (2007, background): “13 Several recent publications 16–18 introduced a two-step approach reducing the complexity of the docking problem.”
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions (2012, neutral): “There are some other databases related to the current study, such as Relibase (10) (http://relibase.rutgers.edu) for ligand– protein interactions, Pocketome (11) (http://pocketome. org/) for druggable binding sites, ProtChemSI (12) (http://pcidb.russelllab.org/) for the network of protein– chemical ”
PocketPicker: analysis of ligand binding-sites with shape descriptors (2007, background): “DrugSite [24] and Pocket-Finder [25] evaluate shape and physicochemical properties for identification of ligand binding envelopes.”
Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure (2009, methodology): “Third, we show that ConCavity performs similarly when using a variety of pocket detection algorithms [14,16,23] or sequence conservation measures [2,26].”