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

Date Published: September 13, 2017

Publisher: Springer Berlin Heidelberg

Author(s): Boxin Guan, Changsheng Zhang, Jiaxu Ning.

http://doi.org/10.1186/s13568-017-0476-0

Abstract

Protein–ligand docking plays an important role in computer-aided pharmaceutical development. Protein–ligand docking can be defined as a search algorithm with a scoring function, whose aim is to determine the conformation of the ligand and the receptor with the lowest energy. Hence, to improve an efficient algorithm has become a very significant challenge. In this paper, a novel search algorithm based on crossover elitist preservation mechanism (CEP) for solving protein–ligand docking problems is proposed. The proposed algorithm, namely genetic algorithm with crossover elitist preservation (CEPGA), employ the CEP to keep the elite individuals of the last generation and make the crossover more efficient and robust. The performance of CEPGA is tested on sixteen molecular docking complexes from RCSB protein data bank. In comparison with GA, LGA and SODOCK in the aspects of lowest energy and highest accuracy, the results of which indicate that the CEPGA is a reliable and successful method for protein–ligand docking problems.

Partial Text

Protein–ligand docking is one of the most important methods in structure-based pharmaceutical development (Brooijmans and Kuntz 2003; Huang and Zou 2010; Jug et al. 2015; Moitessier et al. 2008; Zhao et al. 2014, 2016), and it is also an important approach for large-scale virtual screening. With the development of X-ray technology, the three-dimensional structure of docked conformations has been obtained so that protein–ligand docking has more practical significance. Through the establishment of protein–ligand docking model, and researching the interaction the receptor and the ligand, to discover and design a more effective, more ideal drugs. The process of molecular docking is to search conformations of the proteins and the ligands with lowest energy. The ligands are placed at the active site of the protein receptors, and reasonable orientations and conformations are sought to match the shape and interaction of ligands and receptors. The active binding site refers to a specific small region in the receptors, which is composed of a small number of amino acid residues on the side chain. The optimized target energy value of molecular docking is obtained by calculating the interaction between the ligands and the binding region of the receptors.

To value the impact of the presented algorithm, the performance found by CEPGA with GA, LGA, SODOCK and ABC is compared. The semi-empirical free energy force field described above is used in all experiments in this paper. In order to maintain the diversity of the protein–ligand X-ray structures, theses instances should have a wide span of the number of rotatable bonds in ligands. Sixteen protein–ligand X-ray structures (Hu et al. 2004) with 0–15 rotatable bonds in ligands are chosen from RCSB protein data bank (Berman et al. 2002) (http://www.rcsb.org/pdb) to compare the capability of the docking techniques.

Drug molecular design plays a decisive role in the development of drugs. Protein–ligand docking is the major method of computer aided drug design (Guedes et al. 2014; Huang and Zou 2010), which takes advantage of the combination of drug chemistry and computer technology to improve the efficiency of drug development (Zhao et al. 2008, 2011). The aim of protein–ligand docking is to find the best ligand conformation of a ligand against a protein target with the lowest energy (Bohlooli et al. 2017). many researchers have made great efforts to improve the power of the protein–ligand docking methods, such as simulated annealing (SA), genetic algorithm (GA) (Jones et al. 1997), Lamarckian genetic algorithm (LGA) (Fuhrmann et al. 2010), SODOCK (Chen et al. 2007), and artificial bee colony (ABC) (Uehara et al. 2015). However, the quality of the solutions that the existing algorithms obtain is insufficient. This paper illustrates a novel and robust optimization algorithm (CEPGA) for solving the protein–ligand docking problems with an aim to overcome the above-mentioned drawback.

 

Source:

http://doi.org/10.1186/s13568-017-0476-0

 

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