PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking

Abstract

Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and e±ciently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the e±cient BroydenFletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time e±ciency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to noncommercial users at http://cbbio.cis.umac.mo.

The Performance of Several Docking Programs at Reproducing Protein–Macrolide-Like Crystal Structures

“…With the macrolide subset, we estimated how much the new algorithm contained in PSOVina [ 31 ] reduced the calculation time. As shown in Table 3 , in six out of the eight instances there were time saving, operating with the same computer, equipped with an i7-4790 processor.…”

“…Last year, once most of our AD Vina results were at hand, PSOVina, a program that includes a particle swarm optimization algorithm, was released. PSOVina reduces the calculation time of AD Vina even further; since the accuracy is the same [ 31 ], only a few tests have been performed using it. All these packages are free.…”

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

“…The ideal search algorithm should be able to enumerate all possible binding poses between ligands and receptors, but this is difficult to achieve because the search space involved in molecular docking is huge. Many evolutionary computation methods have been presented for solving protein–ligand docking problems [22,23,24,25,26,27,28]: for example, simulated annealing (SA) [29], genetic algorithm (GA) [30], Lamarckian genetic algorithm (LGA) [31], running history information guided genetic algorithm (HIGA) [32], and swarm optimization for highly flexible protein–ligand docking (SODOCK) [33]. These methods have been applied to solve docking problems, but they have some drawbacks.…”

Genetic algorithm with a crossover elitist preservation mechanism for protein–ligand docking

“…Some algorithms have been shown to be very effective for solving the protein–ligand docking problem, and some researchers have improved the power of these docking methods. For example, simulated annealing (SA) (Goodsell and Olson 1990 ), Genetic algorithm (GA) (Cao and Li 2004 ; Jones et al 1997 ; Thomsen 2003 ), Lamarckian genetic algorithm (LGA) (Fuhrmann et al 2010 ), SODOCK (Chen et al 2007 ; Jason et al 2008 ; Ng et al 2015 ), and artificial bee colony algorithm (ABC) (Uehara et al 2015 ). However, to develop an efficient and reliable search algorithm is still a challenge for docking problem.…”