Computer-Aided Drug Design Utilizing Structure Templates Identified by Local Structure Alignment

Abstract

With a rapid increase in the number of high-resolution protein-ligand structures, the known protein-ligand structures can be used to gain insights into how a ligand binds in a target protein. Based on the fact that the structurally similar binding sites share information about their ligands, we have developed a local structure alignment tool, G-LoSA (Graph-based Local Structure Alignment). Using G-LoSA, the known protein-ligand binding-site structure library is searched to detect local structures with similar geometry and physicochemical properties to a query structure regardless of sequence continuity and protein fold. Then, the ligands in the identified complexes are used as templates to predict a binding site and a ligand structure for the target protein. The performance of G-LoSA is validated against benchmark targets. G-LoSA is able to not only predict the ligand binding sites with high accuracy but also identify a single template ligand that is highly similar to the target ligand. In addition, our benchmark analyses show that an assembly of structural fragments from multiple template ligands can be used to design novel ligand structures specific to the target protein. This study clearly indicates that local-structure based binding-site prediction and ligand modeling have potential for de novo ligand design.