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BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

机译:BCL :: Conf:使用基于知识的旋转异构体库进行小分子构象采样

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摘要

The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure–activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank. Likely conformations of these fragments are stored as ‘rotamers’ in analogy to amino acid side chain rotamer libraries used for rapid sampling of protein conformational space. Explicit fragments take into account correlations between multiple torsion bonds and effect of substituents on torsional profiles. A conformational ensemble for small molecules can then be generated by recombining fragment rotamers with a Monte Carlo search strategy. BCL::Conf was benchmarked against other conformer generator methods including Confgen, Moe, Omega and RDKit in its ability to recover experimentally determined protein bound conformations of small molecules, diversity of conformational ensembles, and sampling rate. BCL::Conf recovers at least one conformation with a root mean square deviation of 2 Å or better to the experimental structure for 99 % of the small molecules in the Vernalis benchmark dataset. The ‘rotamer’ approach will allow integration of BCL::Conf into respective computational biology programs such as Rosetta.>Graphical abstract:Conformation sampling is carried out using explicit fragment conformations derived from crystallographic structure databases. Molecules from the database are decomposed into fragments and most likely conformations/rotamers are used to sample correspondng sub-structure of a molecule of interest.
机译:小分子与蛋白质靶标的相互作用取决于其采用互补的三维结构的能力。因此,对于小分子可以构象的空间的完整,快速的预测对于基于结构和配体的药物发现算法(例如小分子对接或三维定量结构-活性关系)都是至关重要的。在这里,我们得到了剑桥结构数据库和蛋白质数据库中实验结构中经常采样的小分子片段数据库。这些片段的构象可能以“旋转子”的形式存储,类似于用于蛋白质构象空间快速采样的氨基酸侧链旋转异构体库。显式片段考虑了多个扭转键之间的相关性以及取代基对扭转轮廓的影响。然后可以通过将片段旋转异构体与Monte Carlo搜索策略重组来生成小分子的构象集合。 BCL :: Conf在恢复实验确定的小分子蛋白结合构象的能力,构象多样性的多样性和采样率方面,已与Confgen,Moe,Omega和RDKit等其他构象生成器方法进行了基准比较。 BCL :: Conf恢复Vernalis基准数据集中99%小分子实验结构的至少一个均方根偏差为2Å或更好的构象。 “旋转器”方法将允许将BCL :: Conf集成到相应的计算生物学程序中,例如Rosetta。<!-fig ft0-> <!-fig @ position =“ anchor” mode = article f4-> < !-fig mode =“ anchored” f5-> >图形摘要:<!-fig / graphic | fig / alternatives / graphic mode =“ anchored” m1-> <!-标题a7->使用衍生自晶体结构数据库的显式片段构象进行构象取样。来自数据库的分子被分解成碎片,最有可能的构象/旋转异构体被用来采样感兴趣分子的相应子结构。

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