ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization.
Mohammad Reza Zare ShahnehMichael StrobelGiovanni Andrea VitaleChristian GeibelYasin El AbieadNeha GargBerenike WagnerKarl ForchhammerAllegra AronVanessa V PhelanDaniel PetrasMingxun WangPublished in: Journal of the American Society for Mass Spectrometry (2024)
Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.
Keyphrases
- ms ms
- liquid chromatography tandem mass spectrometry
- ultra high performance liquid chromatography
- high throughput
- high performance liquid chromatography
- tandem mass spectrometry
- machine learning
- healthcare
- liquid chromatography
- small molecule
- computed tomography
- mass spectrometry
- density functional theory
- deep learning
- molecular dynamics
- solid phase extraction
- gas chromatography
- optical coherence tomography