School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.
Bioinformatics. 2018 Jul 1;34(13):2209-2218. doi: 10.1093/bioinformatics/bty081.
Sequence-order independent structural comparison, also called structural alignment, of small ligand molecules is often needed for computer-aided virtual drug screening. Although many ligand structure alignment programs are proposed, most of them build the alignments based on rigid-body shape comparison which cannot provide atom-specific alignment information nor allow structural variation; both abilities are critical to efficient high-throughput virtual screening.
We propose a novel ligand comparison algorithm, LS-align, to generate fast and accurate atom-level structural alignments of ligand molecules, through an iterative heuristic search of the target function that combines inter-atom distance with mass and chemical bond comparisons. LS-align contains two modules of Rigid-LS-align and Flexi-LS-align, designed for rigid-body and flexible alignments, respectively, where a ligand-size independent, statistics-based scoring function is developed to evaluate the similarity of ligand molecules relative to random ligand pairs. Large-scale benchmark tests are performed on prioritizing chemical ligands of 102 protein targets involving 1 415 871 candidate compounds from the DUD-E (Database of Useful Decoys: Enhanced) database, where LS-align achieves an average enrichment factor (EF) of 22.0 at the 1% cutoff and the AUC score of 0.75, which are significantly higher than other state-of-the-art methods. Detailed data analyses show that the advanced performance is mainly attributed to the design of the target function that combines structural and chemical information to enhance the sensitivity of recognizing subtle difference of ligand molecules and the introduces of structural flexibility that help capture the conformational changes induced by the ligand-receptor binding interactions. These data demonstrate a new avenue to improve the virtual screening efficiency through the development of sensitive ligand structural alignments.
http://zhanglab.ccmb.med.umich.edu/LS-align/.
Supplementary data are available at Bioinformatics online.
计算机辅助虚拟药物筛选通常需要对小分子配体分子进行序列顺序无关的结构比较，也称为结构比对。虽然已经提出了许多配体结构比对程序，但它们中的大多数都是基于刚体形状比对构建的，这种比对方法不能提供原子特异性的比对信息，也不允许结构发生变化；这两种能力对于高效的高通量虚拟筛选至关重要。
我们提出了一种新的配体比较算法 LS-align，通过目标函数的迭代启发式搜索来生成快速而准确的配体分子原子级结构比对，该目标函数结合了原子间距离以及质量和化学键的比较。LS-align 包含刚体 LS-align 和 Flexi-LS-align 两个模块，分别用于刚体和柔性比对，其中开发了一种配体大小无关的基于统计的评分函数，用于评估相对于随机配体对的配体分子的相似性。在对来自 DUD-E（增强型有用诱饵数据库）数据库的 102 个蛋白质靶标中的 1415871 个候选化合物的化学配体进行优先级排序的大规模基准测试中，LS-align 的平均富集因子（EF）为 22.0，在 1%截止值处的 AUC 评分为 0.75，显著高于其他最先进的方法。详细数据分析表明，先进的性能主要归因于目标函数的设计，该函数结合了结构和化学信息，以提高识别配体分子细微差异的敏感性，并引入了结构灵活性，有助于捕获配体-受体结合相互作用引起的构象变化。这些数据表明，通过开发敏感的配体结构比对，可以提高虚拟筛选效率。
http://zhanglab.ccmb.med.umich.edu/LS-align/。
补充数据可在 Bioinformatics 在线获得。
