Benchmarking Docking Protocols on Predicting Alternative Binding Modes

Abstract

The quality of protein-ligand binding affinity prediction is often limited by the accuracy of positioning the ligand correctly inside the binding site. However, pose accuracy is a secondary concern in high-throughput virtual screening, which is the application scenario in mind when most docking protocols are developed. On the other hand, similar protocols are also applied to position ligands in the binding pocket prior to free-energy calculations, and the accuracy of docking protocols is not well known in this case, especially when structures of analogues are available as templates to guide the docking as is typical in lead optimization. Docking benchmarks typically focus on the ability of docking methods to generate poses with a low RMSD to crystal structures, but for physics-based affinity prediction methods, we also need the ability to identify potential alternate binding modes of a new ligand that might be viable. This is especially relevant for ligand modifications which break local symmetry, leading to multiple potential substituent orientations, such as substitutions of phenyl rings. Here, our focus is on assessment of pose prediction methods when the bound structure of a reference ligand is known (typical in structure-based drug design) and the likely binding mode(s) of a related compound are needed, and we focus on cases where the new compound has multiple potential binding modes. To assess templated docking protocols on their ability to identify binding modes when starting from a solid reference structure, we collected a set of 60 complex structures from the PDB which have more than one ligand binding mode – shown in the PDB records as alternative locations in the ligand. Our results suggest success rates of only 30-50% for finding alternative binding modes, which are modest compared to benchmarks of the same docking programs on the Astex diverse set (70-90% success rates). Overall, we conclude that docking methods would benefit from further tuning or improvement to become more effective in lead optimization.