Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America.
NPPNS, Department of Physic and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil.
PLoS Comput Biol. 2018 Apr 18;14(4):e1006089. doi: 10.1371/journal.pcbi.1006089. eCollection 2018 Apr.
The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.
小分子的注释是无靶标质谱分析中最具挑战性和最重要的步骤之一，因为我们的大多数生物学解释都依赖于结构注释。分子网络已经成为一种有组织的方法，可以对无靶标串联质谱 (MS/MS) 实验的数据进行组织和挖掘，并已广泛应用于传播注释。然而，这种传播是通过手动检查连接在光谱网络中的 MS/MS 光谱来完成的，只有在有参考库光谱的情况下才可行。另一种用于注释未知碎片质谱的方法是通过使用计算预测。计算注释的挑战之一是在预测候选列表中正确结构的不确定性。在这里，我们展示了如何通过传播结构注释来提高计算预测的准确性，即使在光谱库中没有与 MS/MS 光谱匹配的情况下，也可以通过使用分子网络拓扑结构和结构相似性来实现。这是通过使用分子网络拓扑结构和结构相似性对重新排序的结构候选物进行网络共识来实现的，以提高计算注释的准确性。Network Annotation Propagation (NAP) 工具可通过 GNPS 网络平台 https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp 访问。
