SODOCK: Swarm optimization for highly flexible protein-ligand docking. Export

@article{citeulike:1030365, abstract = {Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A. (c) 2006 Wiley Periodicals, Inc. J Comput Chem 28: 612-623, 2007.}, address = {Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan.}, author = {Chen, H. M. and Liu, B. F. and Huang, H. L. and Hwang, S. F. and Ho, S. Y.}, citeulike-article-id = {1030365}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/jcc.20542}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17186483}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17186483}, day = {30}, doi = {10.1002/jcc.20542}, issn = {0192-8651}, journal = {J Comput Chem}, keywords = {docking, docking\_design, search}, month = {January}, number = {2}, pages = {612--623}, posted-at = {2007-01-09 23:05:52}, priority = {3}, title = {SODOCK: Swarm optimization for highly flexible protein-ligand docking.}, url = {http://dx.doi.org/10.1002/jcc.20542}, volume = {28}, year = {2007} }

TY - JOUR ID - citeulike:1030365 L3 - citeulike-article-id:1030365 N2 - Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A. (c) 2006 Wiley Periodicals, Inc. J Comput Chem 28: 612-623, 2007. IS - 2 JF - J Comput Chem SN - 0192-8651 AD - Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan. EP - 623 TI - SODOCK: Swarm optimization for highly flexible protein-ligand docking. VL - 28 SP - 612 KW - docking KW - docking_design KW - search AU - Chen, HM AU - Liu, BF AU - Huang, HL AU - Hwang, SF AU - Ho, SY PY - 2007/01/30/ UR - http://dx.doi.org/10.1002/jcc.20542 ER -