Liu Xiaofeng, Bai Fang, Ouyang Sisheng, Wang Xicheng, Li Honglin, Jiang Hualiang
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China.
BMC Bioinformatics. 2009 Mar 31;10:101. doi: 10.1186/1471-2105-10-101.
Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm.
The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1.
On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.
构象生成是分子建模中普遍存在的问题。许多应用需要对广阔的分子构象空间进行采样或识别生物活性构象以确保成功。为解决该问题已提出了众多计算机模拟方法,从确定性方法到非确定性方法,从系统性方法到随机性方法。在本研究中,我们描述了一种基于多目标进化算法的高效构象采样方法——Cyndi。
构象扰动在由二面角扭转编码的基因组上进行进化操作。设定了各种目标以使生成的帕累托最优构象在能量上更有利且均匀分布于构象空间。添加了一个关于分子伸展程度的可选目标,以获得已观察到会影响分子生物活性的几何伸展或紧凑构象(《计算机辅助分子设计杂志》2002年,16卷:105 - 112页)。针对由329个小分子组成的测试集测试Cyndi的性能,结果显示与相应生物活性构象的平均最小均方根偏差为0.864 Å,表明Cyndi与其他构象生成方法相比具有很强的竞争力。同时,其高速性能(每个分子0.49 ± 0.18秒)使Cyndi成为用于构象数据库制备的实用工具包,并便于后续的药效团映射或刚性对接。附加文件1中提供了Cyndi预编译可执行文件的副本以及mol2格式的测试集分子。
基于多目标进化算法,我们提出了一种新的、高效的构象生成方法Cyndi,并报告了与其他四种方法相比的验证和性能研究结果。结果表明,Cyndi能够生成几何形状多样的构象,并且在针对329个结构重现生物活性构象方面优于其他四种多构象生成器。速度优势表明Cyndi是用于广泛构象采样和大规模构象数据库制备的强大替代方法。