Identifying the unknowns: towards structural elucidation of small molecules using mass spectrometry

识别未知数：利用质谱法阐明小分子的结构

• 批准号: 242259350
• 负责人: Professor Dr. Sebastian Böcker
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/242259350?language=en
• 关键词: Identifying; unknowns; towards; structural; elucidation

Rapid identification of small compounds from small amounts of substance is of interest in many areas of biology and medicine such as metabolomics, drug discovery, biomarker discovery, and diagnostics. Today, mass spectrometry (MS) is a key technology for the identification of small molecules. Due to immense technological advances in mass spectrometry over the last years, the amount and complexity of MS data has been growing rapidly. Computational analysis of such data is presumed to be one of the major technological hurdles in metabolomics today. We proposed fragmentation trees (FTs) as a model for the fragmentation reactions in the measurement.In the first two years of the project, we have improved the FT calculation, extended the fragmentation tree concept to multistage mass spectrometry, and developed a method to predict fragmentation trees from high resolution GC-MS data. Most importantly, we developed a concept to align such fragmentation trees. The resulting alignment scores were used in various applications, among them the database similarity search tool FT-BLAST. FT alignments outperformed standard spectral comparison in these applications.In the following two years, we will continue to improve the quality of FTs from several analytical platforms as well as the quality of the alignments. We will develop statistically meaningful scores for both the calculation and alignment of FTs. We will provide an internet platform so that fragmentation trees and losses can be evaluated by MS experts. We will construct biochemical reaction networks from MS data. Finally, we will make the developed methods available to mass spectrometrists by an easy-to-use software tool.

从少量物质中快速鉴定小化合物在生物学和医学的许多领域中是令人感兴趣的，例如代谢组学、药物发现、生物标志物发现和诊断。 如今，质谱（MS）是鉴定小分子的关键技术。 由于过去几年质谱技术的巨大进步，MS数据的数量和复杂性迅速增长。 对这些数据的计算分析被认为是当今代谢组学的主要技术障碍之一。我们提出了碎片树（fragmentation tree，FT）作为测量中碎片反应的模型，在项目的前两年，我们改进了FT计算，将碎片树的概念扩展到多级质谱，并发展了一种从高分辨率GC-MS数据预测碎片树的方法。最重要的是，我们开发了一个概念来对齐这种碎片树。所得比对分数用于各种应用中，其中包括数据库相似性搜索工具FT-BLAST。 在这些应用中，FT比对的表现优于标准光谱比较。在接下来的两年里，我们将继续提高多个分析平台的FT质量以及比对质量。我们将为FT的计算和对齐制定具有统计学意义的评分。我们将提供一个互联网平台，以便MS专家可以评估碎片树和损失。我们将从MS数据构建生化反应网络。 最后，我们将通过一个易于使用的软件工具，使开发的方法提供给质谱师。