Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking.
Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking.
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DOI：
10.1186/1471-2105-15-234
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发表时间：
2014-07-05
影响因子：
3
通讯作者：
Steinbeck C
中科院分区：
文献类型：
--
作者：
Jayaseelan KV;Steinbeck C
在代谢组学实验中，通常检测结构未知的代谢物的光谱指纹。计算机辅助结构解析（CASE）已被用于确定未知化合物的结构身份。通常认为，单个1D NMR光谱或质谱通常不足以确定迄今未知化合物的身份。当一套光谱从1D和2D NMR实验补充了分子式，成功阐明的化学结构的候选人与多达30个重原子之前已报告的作者之一。在高通量代谢组学中，通常仅进行1D NMR或质谱实验以快速分析样品。该方法随后要求自动分析光谱图案以快速识别已知和未知结构。在这项研究中，我们调查了是否额外的现有知识，如未知化合物是一种天然产物的事实，可以用来提高正确的结构在结构解析过程后的结果列表中的排名。为了使用尽可能少的光谱信息来识别未知物，我们实施了一种基于进化算法的CASE机制，以完全自动化的方式阐明候选物，并输入分离化合物的分子式和13 C NMR光谱。我们还测试了如何过滤器，如天然产物相似性，一种计算化合物与已知天然产物空间相似性的措施，可能会提高结构解析的性能和质量。进化算法在先前报道的CASE的SENECA包中实现，并且可以在http://sourceforge.net/projects/seneca/上以艺术许可证免费下载。自然产品相似度计算器作为SENECA中的插件合并，并作为GUI客户端和命令行可执行文件提供。显着改善候选人的排名证明了41个小的测试分子的情况下，系统是由一个天然的产品相似的过滤器补充。在光谱欠定结构解析问题中，天然产物相似性可以有助于在结果列表中更好地排列正确的结构。
In metabolomics experiments, spectral fingerprints of metabolites with no known structural identity are detected routinely. Computer-assisted structure elucidation (CASE) has been used to determine the structural identities of unknown compounds. It is generally accepted that a single 1D NMR spectrum or mass spectrum is usually not sufficient to establish the identity of a hitherto unknown compound. When a suite of spectra from 1D and 2D NMR experiments supplemented with a molecular formula are available, the successful elucidation of the chemical structure for candidates with up to 30 heavy atoms has been reported previously by one of the authors. In high-throughput metabolomics, usually 1D NMR or mass spectrometry experiments alone are conducted for rapid analysis of samples. This method subsequently requires that the spectral patterns are analyzed automatically to quickly identify known and unknown structures. In this study, we investigated whether additional existing knowledge, such as the fact that the unknown compound is a natural product, can be used to improve the ranking of the correct structure in the result list after the structure elucidation process. To identify unknowns using as little spectroscopic information as possible, we implemented an evolutionary algorithm-based CASE mechanism to elucidate candidates in a fully automated fashion, with input of the molecular formula and 13C NMR spectrum of the isolated compound. We also tested how filters like natural product-likeness, a measure that calculates the similarity of the compounds to known natural product space, might enhance the performance and quality of the structure elucidation. The evolutionary algorithm is implemented within the SENECA package for CASE reported previously, and is available for free download under artistic license at http://sourceforge.net/projects/seneca/. The natural product-likeness calculator is incorporated as a plugin within SENECA and is available as a GUI client and command-line executable. Significant improvements in candidate ranking were demonstrated for 41 small test molecules when the CASE system was supplemented by a natural product-likeness filter. In spectroscopically underdetermined structure elucidation problems, natural product-likeness can contribute to a better ranking of the correct structure in the results list.
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DOI：
10.1021/ci025584y
发表时间：
2003-03
期刊：
Journal of chemical information and computer sciences
影响因子：
--
作者：
Steinbeck C;Han Y;Kuhn S;Horlacher O;Luttmann E;Willighagen E
通讯作者：
Willighagen E
DOI：
10.1021/ci950179a
发表时间：
1996-07-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Faulon, JL
通讯作者：
Faulon, JL
影响因子：
2
作者：
Blinov, KA;Carlson, D;Williams, AJ
通讯作者：
Williams, AJ
影响因子：
2
作者：
BREMSER, W
通讯作者：
BREMSER, W
影响因子：
3.7
作者：
Peironcely JE;Reijmers T;Coulier L;Bender A;Hankemeier T
通讯作者：
Hankemeier T