MS2DeepScore - a novel deep learning similarity measure for mass fragmentation spectrum comparisons

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are considered characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of >100,000 mass spectra of about 15,000 unique known compounds, MS2DeepScore learns to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3,600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and predicts Tanimoto scores with a root mean squared error of about 0.15. The prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. We demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity metrics have great potential for a range of metabolomics data processing pipelines.

Systematical Characterization of Impurity Profiles in Daptomycin Raw Material by 2-Dimentional HPLC Tandem with MS Detector

Objective: To systematically characterize the impurity profile in Daptomycin raw material by 2 Dimensional LC/MSn. Method: The target impurity was separated by first Dimensional HPLC and enriched by a 500μl loop, then desalted using the on-line second Dimensional HPLC and analyzed by MS detector in positive mode. Their structures were characterized based on the degradation mechanism and mass fragmentation regularity of the cyclic lipopeptide, as well as the molecular thermodynamic calculation. Results: A total of 12 impurities were characterized in the raw material, including 6 degradation products; 8 impurities are reported for the first time. The mass fragmentation regularities of 2 β-isomers of Asp residue were summarized. Conclusion: The structures of impurities in Daptomycin raw material, especially for β-isomer impurities, could be rapidly identified by on-line 2 Dimensional LC/MSn method together with the molecular thermodynamic calculation.