Shahneh Mohammad Reza Zare, Pyke James, Rennie Emma E, Wang Mingxun
Department of Computer Science and Engineering, University of California Riverside, 900 University Avenue, Riverside, California 92521, United States.
Agilent Technologies, Inc., 11011 N Torrey Pines Road, La Jolla, California 92037, United States.
J Am Soc Mass Spectrom. 2025 May 7;36(5):999-1007. doi: 10.1021/jasms.4c00464. Epub 2025 Apr 9.
Tandem mass spectrometry (MS/MS) is a powerful technique for structural identification of small molecules, yet a significant portion of MS/MS spectra from untargeted experiments remain unidentifiable through spectrum library matching. ModiFinder, a computational tool, tackles this issue by predicting the site of chemical modifications on known analogs of the unidentified compounds using MS/MS data. However, ModiFinder's performance is limited by insufficient peak data and fragmentation annotation ambiguities. In this study, we investigate how incorporating MS/MS spectra from multiple collision energies and mass spectrometry adducts can enhance ModiFinder's localization accuracy. Using a data set from Agilent Technologies comprising 2150 data-rich compounds (five times larger than previously available data sets), we evaluated the impact of complementary spectral information. Our results show that combining spectra from different adducts and collision energies expands ModiFinder's localization abilities to more compounds and improves the overall performance.
串联质谱（MS/MS）是一种用于小分子结构鉴定的强大技术，但通过光谱库匹配，非靶向实验产生的MS/MS光谱中有很大一部分仍然无法识别。计算工具ModiFinder通过使用MS/MS数据预测未识别化合物的已知类似物上的化学修饰位点来解决这个问题。然而，ModiFinder的性能受到峰值数据不足和碎片注释模糊性的限制。在本研究中，我们研究了合并来自多个碰撞能量和质谱加合物的MS/MS光谱如何提高ModiFinder的定位准确性。使用安捷伦科技公司提供的包含2150种数据丰富化合物的数据集（比以前可用的数据集大五倍），我们评估了互补光谱信息的影响。我们的结果表明，结合来自不同加合物和碰撞能量的光谱可将ModiFinder的定位能力扩展到更多化合物，并提高整体性能。
