Efficient protein-ligand docking using sustainable evolutionary algorithms

Abstract

AutoDock is a widely used automated protein docking program in structure-based drug-design. Different search algorithms such as simulated annealing, traditional genetic algorithm (GA) and Lamarckian genetic algorithm (LGA) are implemented in AutoDock. However, the docking performance of these algorithms is still limited by the local optima issue of simulated annealing or the premature convergence issue typical in traditional evolutionary algorithms (EA). Due to the stochastic nature of these search algorithms, users usually need to run multiple times to get reasonable docking results, which is time-consuming. We have developed a new docking program AutoDockX by applying a sustainable GA, Age-Layered Population Structure (ALPS) to the protein docking problem. We tested the docking performance over three different proteins (pr, cox and hsp90) with more than 20 candidate ligands for each protein. Our experiments showed that the sustainable GA based AutodockX achieved significantly better docking performance in terms of running time and robustness than all the existing search algorithms implemented in the latest version of AutoDock. AutodockX thus has unique advantages in large-scale virtual screening.