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都很有兴趣，如代谢组学、生物勘探、生物标志物发现和诊断。如今，质谱法（MS）是鉴定小分子的关键技术。由于质谱技术在过去几年中取得了巨大的进步，质谱数据的数量和复杂性一直在迅速增长。串联质谱法提供了仅从质谱指纹中识别小分子的潜力，但对此类数据的计算分析尚处于起步阶段，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“识别未知”的计算方法，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、模型和算法来解释来自小分子的质谱碎片数据。作为一个中期目标，我们想开发一个类似blast的搜索工具来搜索碎片模式。事实证明，许多出现的问题在计算上是困难的，并且需要新的算法概念来保证可以找到最优解。我们将实施、培训和评估我们的方法，以实现代谢物质谱数据的自动化处理。Stephan Clemens教授（拜罗伊特大学）和Christoph博士Böttcher （IPB Halle）计划申请资金，利用为关联图谱开发的拟南芥群体，在全基因组范围内研究次生代谢的遗传控制。我们计划在这个问题上与他们的小组密切合作。

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.