Binding ligand prediction by comparing local surface patches of potential pocket regions
Drug Designing: Open Access

Drug Designing: Open Access
Open Access

ISSN: 2169-0138

Binding ligand prediction by comparing local surface patches of potential pocket regions

International Conference and Exhibition on Computer Aided Drug Design & QSAR

October 29-31, 2012 DoubleTree by Hilton Chicago-North Shore, USA

Daisuke Kihara

Accepted Abstracts: Drug Design

Abstract :

Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, each of which is characterized by its geometrical shape, the electrostatic potential, the hydrophobicity, and the concaveness. Representing a pocket by a set of patches is effective to absorb difference of global pocket shape while capturing local similarity of pockets. The shape and the physicochemical properties of surface patches are represented using the 3D Zernike descriptor, which is a series expansion of mathematical 3D function. Two pockets are compared using a modified weighted bipartite matching algorithm, which matches similar patches from the two pockets. Patch-Surfer showed superior performance to existing methods including a global pocket comparison method, Pocket-Surfer, which we have previously introduced. Particularly, as intended, the accuracy showed large improvement for flexible ligand molecules, which bind to pockets in different conformations. The developed method can be applied for rapid searching of drug molecules that bind to a binding pocket of a target protein. This work is supported by grants from NIH (R01GM075004, R01GM097528).

Biography :

Daisuke Kihara is associate professor of Biological Sciences and Computer Science at Purdue University. His research interests are development and application of structural bioinformatics methods, including those for protein structure modeling, protein structure-based function prediction, protein-protein docking, and protein-ligand docking. His research projects are supported by the National Institutes of Health and the National Science Foundation, and Purdue Research Foundation. He has published more than 70 papers in reputed journals.