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Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This research presents PoCavEDT, an automated purely geometric technique for the identification of binding pockets and occluded cavities in proteins based on the 3D Euclidean Distance Transform. Candidate protein pocket regions are identified between two Solvent-Excluded surfaces generated with the Euclidean Distance Transform using different probe spheres, which depend on the size of the binding ligand. The application of simple, yet effective geometrical heuristics ensures that the proposed method obtains very good ligand binding site prediction results. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities. Its performance was compared to the results achieved with several purely geometric pocket and cavity prediction methods, namely SURFNET, PASS, CAST, LIGSITE, LIGSITECS, PocketPicker and POCASA. Success rates PoCavEDT were comparable to those of POCASA and outperformed the other software.
This is an extended abstract which has been accepted for the BBCC2018 Conference.