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

从少量物质中快速识别小化合物在代谢组学、药物发现、生物标记物发现和诊断学等生物学和医学领域中具有重要意义。如今，质谱学(MS)是鉴定小分子的关键技术。由于过去几年在质谱学方面的巨大技术进步，MS数据的数量和复杂性一直在快速增长。这类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。在项目的前两年，我们改进了碎裂树的计算方法，将碎裂树的概念扩展到多级质谱学中，并开发了一种从高分辨率GC-MS数据预测碎裂树的方法。最重要的是，我们开发了一个概念来对齐这种碎片化树。所得到的比对分数用于各种应用，其中包括数据库相似性搜索工具FT-BLAST。FT比对在这些应用中的表现优于标准光谱比较。在接下来的两年里，我们将继续提高几个分析平台的FT质量以及比对的质量。我们将为FTS的计算和排列制定具有统计意义的分数。我们将提供一个互联网平台，以便MS专家可以评估碎片化树和损失。我们将从MS数据中构建生化反应网络。最后，我们将通过一个简单易用的软件工具将开发的方法提供给质谱学家。

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.