A New Approach for Flexible Molecular Docking Based on Swarm Intelligence

Yi Fu,Ji Zhao,Xiaojun Wu,Wenbo Xu,Zhiguo Chen,Jun Sun

doi:10.1155/2015/540186

Abstract

Molecular docking methods play an important role in the field of computer-aided drug design. In the work, on the basis of the molecular docking program AutoDock, we present QLDock as a tool for flexible molecular docking. For the energy evaluation, the algorithm uses the binding free energy function that is provided by the AutoDock 4.2 tool. The new search algorithm combines the features of a quantum-behaved particle swarm optimization (QPSO) algorithm and local search method of Solis and Wets for solving the highly flexible protein-ligand docking problem. We compute the interaction of 23 protein-ligand complexes and compare the results with those of the QDock and AutoDock programs. The experimental results show that our approach leads to substantially lower docking energy and higher docking precision in comparison to Lamarckian genetic algorithm and QPSO algorithm alone. QPSO-ls algorithm was able to identify the correct binding mode of 74% of the complexes. In comparison, the accuracy of QPSO and LGA is 52% and 61%, respectively. This difference in performance rises with increasing complexity of the ligand. Thus, the novel algorithm QPSO-ls may be used to dock ligand with many rotatable bonds with high accuracy.

Highlights

Computer-aided tools have wide applications in various fields, such as biotechnology engineering, medical engineering, mathematical modeling, and electronic information [1,2,3,4]
We propose a novel search method called QPSO-ls for solving highly flexible docking problem, which is a hybrid of quantum-behaved particle swarm optimization (QPSO) and a local search method
QPSO-ls finds this ligand complex with lower binding energy after approximately 140,000 computing steps in comparison to 150,000 computing steps found by the Lamarckian genetic algorithm (LGA)

Summary

Introduction

Computer-aided tools have wide applications in various fields, such as biotechnology engineering, medical engineering, mathematical modeling, and electronic information [1,2,3,4]. New drugs can be designed efficiently by using computer-aided docking algorithm simulations to find highly affine components that bind well to the targeted protein [5, 6]. Docking methods typically use an energy-based scoring function to identify the energetically most favorable ligand conformation when bound to the target. Molecular docking can be viewed as a complex combinatorial optimization problem. We propose a novel search method called QPSO-ls for solving highly flexible docking problem, which is a hybrid of quantum-behaved particle swarm optimization (QPSO) and a local search method.

Algorithm and Scoring Function

Data Preparation and Parameter Setting

Results

Conclusion