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

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、生物勘探、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。串联质谱法提供了仅从MS指纹识别小分子的潜力，但此类数据的计算分析仍处于起步阶段，这被认为是当今代谢组学的主要技术障碍之一。该项目的主要目标是开发计算方法，以“识别未知物”，即在任何数据库中都找不到的化合物。为此，我们将开发新的计算技术，模型和算法，用于解释小分子的MS碎片数据。作为一个中期目标，我们希望开发一个类似BLAST的搜索工具，用于搜索碎片模式。事实证明，许多出现的问题是计算困难，并需要新的算法概念，以保证可以找到最佳的解决方案。我们将实施，训练和评估我们的方法，以允许代谢物MS数据的自动处理。Stephan Clemens教授（拜罗伊特大学）和Christoph Böttcher博士（IPB Halle）计划申请资金，在全基因组范围内研究次级代谢的遗传控制，采用A.为关联作图而开发的拟南芥种群。我们计划就这一问题与他们的团体密切合作。

Rapid identification of small compounds from small amounts of substance is of interest in many areas of biology and medicine such as metabolomics, bio-prospecting, 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. Tandem mass spectrometry offers the potential of identifying small molecules solely from MS fingerprints, but computational analysis of such data is in its infancy, and this is presumed to be one of the major technological hurdles in metabolomics today. The key objective of this project is to develop computational methods to “identify the unknowns”, that is, compounds that cannot be found in any database. For this, we will develop new computational techniques, models, and algorithms for the interpretation of MS fragmentation data from small molecules. As a medium term goal, we want to develop a BLAST-like search tool for searching fragmentation patterns. It turns out that many of the arising problems are computationally hard, and require new algorithmic concepts to guarantee that optimal solutions can be found. We will implement, train, and evaluate our methods to allow an automated processing of metabolite MS data. Prof. Stephan Clemens (Bayreuth University) and Dr. Christoph Böttcher (IPB Halle) plan to apply for funding to study genetic control of secondary metabolism at a genomewide scale, employing A. thaliana populations developed for association mapping. We plan to closely cooperate with their groups on the subject matter.