Abstract
Protein–ligand docking is an optimization problem, which aims to identify the binding pose of a ligand with the lowest energy in the active site of a target protein. Energy landscapes of the scoring functions are usually complicated and exhibit a rugged funnel shape. Hence, successful docking simulations require an efficient optimization algorithm. Various optimization algorithms have been developed for the protein–ligand docking. Genetic algorithm (GA) based approaches are the most general. On the other hand, some variants of particle swarm optimization (PSO) are reported that improve docking accuracy over GA based approaches. In this study, we attempted to apply a novel optimization algorithm, called fitness learning-based artificial bee colony with proximity stimuli (FlABCps), to the protein–ligand docking. The artificial bee colony (ABC) algorithm is a simple and powerful optimization algorithm for the multi-dimensional and multi-modal functions, inspired from intelligent behaviors of honey bee swarm. It has been reported that the ABC based algorithms give better results for various optimization problems than the conventional algorithms. Simulation results revealed that FlABCps improved the success rate of docking, compared to four state-of-the-art algorithms. The present results also showed superior docking performance of FlABCps, in particular for dealing with highly flexible ligands with a number of rotational bonds.
