MS2Query: reliable and scalable MS2 mass spectra-based analogue search

Jonge, Niek F. de; Louwen, Joris J R; Chekmeneva, Elena; Camuzeaux, Stéphane; Vermeir, Femke J.; Jansen, Robert S.; Huber, Florian; Hooft, Justin J. J. van der

doi:10.1038/s41467-023-37446-4

Cited by 29 publications

(12 citation statements)

References 47 publications

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“…The use of oxPUFA data along with our NEO-MSMS library can be highly valuable, both for annotating compounds found in biological matrices and for connecting new structures to discover potential novel biomarkers. The very precise nature of OxPUFA data also likely suggest its use for machine-learning based annotation 51 .…”

Section: Technical Validationmentioning

confidence: 99%

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Elloumi,

Mas-Normand,

Bride

et al. 2024

Sci Data

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Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for many diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) dataset comprising pure NEO-PUFAs (both commercial and self-synthesized) and in vitro free radical-induced oxidation of diverse PUFAs. By employing molecular networking techniques with this dataset and the existent ones in public repositories, we successfully mapped a wide range of NEO-PUFAs, expanding the strategies for annotating oxylipins, and NEO-PUFAs and offering a novel workflow for profiling these molecules in biological samples.

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Section: Technical Validationmentioning

confidence: 99%

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Elloumi,

Mas-Normand,

Bride

et al. 2024

Sci Data

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“…Subsequently, using molecular structural similarity as a training label, they developed the MS2DeepScore model, in which the input mass spectral pairs were directly processed to predict the molecular structural similarity . Recently, by combining the above two approaches, they introduced the MS2Query framework . With the utilization of the spectral features extracted by both Spec2Vec and MS2DeepScore and the incorporation of the molecular information such as precursor ion m / z , the followed random forest regression effectively integrated spectral and molecular features to better predict the molecular structural similarity.…”

Section: Spectrum To Molecular Structurementioning

confidence: 99%

Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives

Lu,

Wu,

et al. 2024

Anal. Chem.

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Spectrum−structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum−structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure−spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum−structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum−structure correlation soon, which would trigger substantial development of various disciplines.

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“…Interpretation of tandem mass spectra is increasingly done with the help of computational tools that assist with structurally annotating mass spectra, such as SIRIUS and MS-Finder 1 . Mass spectral similarity scores, like the cosine score, modified cosine score 2 , Spec2Vec 3 , MS2DeepScore 4 , MS2Query 5 , and others 6,7 , play a crucial role in common methods like library matching, analogue searching, and organizing spectra by molecular networking.…”

Section: Introductionmentioning

confidence: 99%

Reliable cross-ion mode chemical similarity prediction between MS²spectra

de Jonge,

Joas,

Truong

et al. 2024

Preprint

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Mass spectrometry is commonly used to characterize metabolites in untargeted metabolomics. This can be done in positive and negative ionization mode, a choice typically guided by the fraction of metabolites a researcher is interested in. During analysis, mass spectral comparisons are widely used to enable annotation through reference libraries and to facilitate data organization through networking. However, until now, such comparisons between mass spectra were restricted to mass spectra of the same ionization mode, as the two modes generally result in very distinct fragmentation spectra. To overcome this barrier, here, we have implemented a machine learning model that can predict chemical similarity between spectra of different ionization modes. Hence, our new MS2DeepScore 2.0 model facilitates the seamless integration of positive and negative ionization mode mass spectra into one analysis pipeline. This creates entirely new options for data exploration, such as mass spectral library searching of negative ion mode spectra in positive ion mode libraries or cross-ionization mode molecular networking. Furthermore, to improve the reliability of predictions and better cope with unseen data, we have implemented a method to estimate the quality of prediction. This will help to avoid false predictions on spectra with low information content or spectra that substantially differ from the training data. We anticipate that the MS2DeepScore 2.0 model will extend our current capabilities in organizing and annotating untargeted metabolomics profiles.

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MS2Query: reliable and scalable MS2 mass spectra-based analogue search

Cited by 29 publications

References 47 publications

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives

Reliable cross-ion mode chemical similarity prediction between MS²spectra

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MS2Query: reliable and scalable MS2 mass spectra-based analogue search

Cited by 29 publications

References 47 publications

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives

Reliable cross-ion mode chemical similarity prediction between MS2spectra

Contact Info

Product

Resources

About

Reliable cross-ion mode chemical similarity prediction between MS²spectra