Detecting local ligand‐binding site similarity in nonhomologous proteins by surface patch comparison

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 was benchmarked on three datasets, which consist in total of 390 proteins that bind to one of 21 ligands. 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.

Computational Methods in Drug Discovery

“…Pocket matching. Methods like Catalytic Site Atlas (Porter et al, 2004), AFT (Arakaki et al, 2004), SURFACE (Ferre et al, 2004), POCKET-SURFER (Chikhi et al, 2010), and PATCH-SURFER (Sael and Kihara, 2012) detect similar pockets based on reference ligand binding sites. Catalytic Site Atlas contains annotated descriptors of enzyme active site residues as well as equivalent sites in related proteins found by sequence alignment.…”

Cardiolipin Interactions with Proteins

“…Molecular modeling has been used extensively to predict interactions between small molecules and proteins, essential for example in the drug discovery process (22). Identifying and studying the BPs of specific ligands to a group of proteins helps understanding of the molecular mechanisms governing such interactions (23). In turn, this knowledge may help in the design of proteins with specific functions (24,25), and in development of inhibitors and drugs targeting the identified binding region (26).…”

Computational characterization of moonlighting proteins

“…Although a drawback of structural analysis is that it is only applicable to proteins that have experimentally solved tertiary structures, it is noteworthy that computationally modeled structures could be used as structure prediction methods have matured in recent years [39–41]. To aid in finding binding sites of moonlighting proteins, methods for detecting binding pocket sites in protein structures [42] and predicting binding ligands [43,44] can be useful.…”