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Submitted on October 11, 2004
Revised on March 7, 2005
Accepted on March 8, 2005

Pocketome: Comprehensive identification and classification of ligand-binding envelopes

Jianghong An, Maxim Totrov, and Ruben Abagyan

Molecular Biology, The Scripps Reseach Institute, La Jolla, CA 92037

Corresponding Author: abagyan{at}scripps.edu

We have developed a new computational algorithm for the accurate identification of ligand-binding envelopes, rather than surface binding sites. We have 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 datasets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank (PDB), 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 database of known and the 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.


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