Identifying the Unknowns: Fragmentation Trees and Molecular Fingerprints

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意义，例如代谢组学、天然产物研究、生物标志物发现、环境研究和诊断。 如今，质谱（MS）是鉴定小分子的关键技术。 在过去的几年里，MS数据的数量和复杂性一直在快速增长。 对这些数据的计算分析被认为是当今代谢组学和小分子研究的主要技术障碍之一。大约十年前，我的小组提出了碎片树作为测量中碎片反应的模型，并开发了SIRIUS来计算这种碎片树。大约五年前，我们开发了CSI：FingerID，用于使用串联MS数据在分子结构数据库中进行搜索，整合碎片树以提高搜索性能。作为上一个资助期的一部分，我们开发了CANOPUS，用于化合物类别的全面分配，而无需结构解析，以及ZODIAC，用于完整数据集中的分子式分配。我们发布了SIRIUS的第4版，被Nature Methods命名为“观察方法”，并展示了我们方法在生物案例研究中的强大功能。在第二个资助期内，我们将进行四个不同的生物案例研究，以展示我们计算工具的强大功能和多功能性;我们将继续将LC-MS/MS处理集成到SIRIUS中;我们将开发Epimetheus用于CSI的验证：FingerID搜索结果;我们将对SIRIUS和CSI：FingerID进行深入评估;我们将完成从上一个资助期分离嵌合光谱的工作;我们将使ZODIAC更快更好;最后，我们将开发一种基于网络的分子指纹校正方法，以进一步提高CSI：FingerID的性能。

Rapid identification of small compounds from small amounts of substance is of interest in many areas of biology and medicine such as metabolomics, natural products research, biomarker discovery, environmental research and diagnostics. Today, mass spectrometry (MS) is a key technology for the identification of small molecules. 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 and small molecule research today.About a decade ago, my group proposed fragmentation trees as a model for the fragmentation reactions in the measurement, and developed SIRIUS to compute such fragmentation trees. About five years ago, we developed CSI:FingerID for searching in molecular structure databases using tandem MS data, integrating fragmentation trees to boost search performance. As part of the previous funding period, we developed CANOPUS of the comprehensive assignment of compound classes without the need for structural elucidation, and ZODIAC for molecular formula assignments in complete datasets. We released version 4 of SIRIUS, which was named "method to watch" by Nature Methods, and demonstrated the power of our methods in biological case studies.In the second funding period, we will perform four diverse biological case studies, to demonstrate the power and versatility of our computational tools; we will continue our integration of LC-MS/MS processing into SIRIUS; we will develop Epimetheus for the validation of CSI:FingerID search results; we will perform in-depth evaluations of SIRIUS and CSI:FingerID; we will complete our work on separating chimeric spectra from the previous funding period; we will make ZODIAC faster and better; and finally, we will develop a method for network-based correction of molecular fingerprints, to further boost CSI:FingerID's performance.