Friday, November 20, 2015
3:00 pm, MRB 100C
Dr. Daisuke Kihara
Department of Biological Sciences/Computer Science, Purdue University
Patch-Surfer and PL-PatchSurfer: Predicting Binding Ligands for Target Proteins by Molecular Surface Similarity and Complementarity
Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of proteins and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins’ biological function and predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer (Zhu, Xiong & Kihara, Bioinformatics 2015), which is a pocket-pocket comparison method, and PL-PatchSurfer (Shin, Bures, & Kihara, Methods, 2015), which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: 1) rotational invariance and 2) fast comparison.