You Qi, Li Chao, Sun Jun, Palade Vasile, Pan Feng
Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Wuxi, Jiangsu, PR China.
Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK.
Mol Inform. 2023 Mar;42(3):e2200080. doi: 10.1002/minf.202200080. Epub 2023 Jan 31.
AutoDock is a widely used software for flexible ligand docking problems since it is open source and easy to be implemented. In this paper, a novel hybrid algorithm is proposed and applied in the docking environment of AutoDock version 4.2.6 in order to enhance the accuracy and the efficiency for dockings with flexible ligands. This search algorithm, called entropy-based Lamarckian quantum-behaved particle swarm optimization (ELQPSO), is a combination of the QPSO with an entropy-based update strategy and the Solis and Wet local search (SWLS) method. By using the PDBbind core set v.2016, the ELQPSO is compared with the Lamarckian genetic algorithm (LGA), Lamarckian particle swarm optimization (LPSO) and Lamarckian QPSO (LQPSO). The experimental results reveal that the corresponding docking program of ELQPSO, named as EQDOCK in this paper, has a competitive performance in dealing with the protein-ligand docking problems. Moreover, for the test cases with different number of torsions, the EQDOCK outperforms the other three docking programs in finding docking conformations with small root mean squared deviation (RMSD) values in most cases. In particular, it has an advantage of solving highly flexible ligand docking problems over the others.
AutoDock是一款广泛用于解决柔性配体对接问题的软件，因为它是开源的且易于实现。本文提出了一种新颖的混合算法，并将其应用于AutoDock 4.2.6版本的对接环境中，以提高柔性配体对接的准确性和效率。这种搜索算法称为基于熵的拉马克量子行为粒子群优化算法（ELQPSO），它是量子行为粒子群优化算法（QPSO）与基于熵的更新策略以及索利斯和韦特局部搜索（SWLS）方法的结合。通过使用PDBbind核心集v.2016，将ELQPSO与拉马克遗传算法（LGA）、拉马克粒子群优化算法（LPSO）和拉马克量子行为粒子群优化算法（LQPSO）进行了比较。实验结果表明，ELQPSO相应的对接程序（本文中命名为EQDOCK）在处理蛋白质-配体对接问题时具有竞争力。此外，对于具有不同扭转数的测试用例，在大多数情况下，EQDOCK在找到具有小均方根偏差（RMSD）值的对接构象方面优于其他三个对接程序。特别是，在解决高度柔性配体对接问题方面，它比其他方法具有优势。
