RT Journal Article
SR Electronic
T1 Bridging polarities in metabolomics: Cross-ionization mode chemical similarity prediction between tandem mass spectra
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.03.25.586580
DO 10.1101/2024.03.25.586580
A1 de Jonge, Niek F.
A1 Chekmeneva, Elena
A1 Schmid, Robin
A1 Joas, David
A1 Truong, Lem-Joe
A1 van der Hooft, Justin J.J.
A1 Huber, Florian
YR 2025
UL http://biorxiv.org/content/early/2025/01/24/2024.03.25.586580.abstract
AB Mass spectrometry is a cornerstone of untargeted metabolomics, enabling the characterization of metabolites in both positive and negative ionization modes. However, comparisons across ionization modes have remained a substantial challenge due to the distinct fragmentation patterns produced by each polarity. To overcome this barrier, we present MS2DeepScore 2.0, a machine learning-based model to predict chemical similarity between mass fragmentation spectra, which works both between different and the same ionization modes. We demonstrate the utility of MS2DeepScore 2.0 in a human urine case study, where MS2DeepScore enabled cross-ionization mode molecular networking, enhancing data exploration and metabolite annotation. To ensure robustness, we have implemented a quality estimation method that flags spectra with low information content or those dissimilar to the training data, thereby minimizing false predictions. Altogether, MS2DeepScore 2.0 extends our current capabilities in organizing, exploring, and annotating untargeted metabolomics profiles.Competing Interest StatementJJJvdH is member of the Scientific Advisory Board of NAICONS Srl., Milano, Italy and consults for Corteva Agriscience, Indianapolis, IN, USA. RS is a co-founder of mzio GmbH, Bremen, Germany. All other authors declare to have no competing interests.