Predicting Protein-Ligand Binding Sites Based on an Improved Geometric Algorithm
Knowledge of protein-ligand binding sites is very important for structure-based drug designs. To get information on the binding site of a targeted protein with its ligand in a timely way, many scientists tried to resort to computational methods. Although several methods have been released in the past few years, their accuracy needs to be improved. In this study, based on the combination of incremental convex hull, traditional geometric algorithm, and solvent accessible surface of proteins, we developed a novel approach for predicting the protein-ligand binding sites. Using PDBbind database as a benchmark dataset and comparing the new approach with the existing methods such as POCKET, Q-SiteFinder, MOE-SiteFinder, and PASS, we found that the new method has the highest accuracy for the Top 2 and Top 3 predictions. Furthermore, our approach can not only successfully predict the protein-ligand binding sites but also provide more detailed information for the interactions between proteins and ligands. It is anticipated that the new method may become a useful tool for drug development, or at least play a complementary role to the other existing methods in this area.
Keywords: Binding site prediction, molecular docking, structure-based drug design, Protein-ligand interactions, geometrybased approach, geometric algorithm, PDBbind, PASS, Q-SiteFinder, MOE-SiteFinder, CASTp, LIGSITE, DSSP program, GORBinding site prediction, molecular docking, structure-based drug design, Protein-ligand interactions, geometrybased approach, geometric algorithm, PDBbind, PASS, Q-SiteFinder, MOE-SiteFinder, CASTp, LIGSITE, DSSP program, GOR
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